Estimating Effects After Weighting

Noah Greifer

2025-02-24

Introduction

After assessing balance and deciding on a weighting specification, it comes time to estimate the effect of the treatment in the weighted sample. How the effect is estimated and interpreted depends on the desired estimand and the effect measure used. In addition to estimating effects, estimating the uncertainty of the effects is critical in communicating them and assessing whether the observed effect is compatible with there being no effect in the population. This guide explains how to estimate effects after weighting for point and longitudinal treatments and with various outcome types.

This guide is structured as follows: first, information on the concepts related to effect and standard error (SE) estimation is presented below. Then, instructions for how to estimate effects and SEs are described for the standard case (weighting for the ATE with a binary treatment and continuous outcome) and some other common circumstances. Finally, recommendations for reporting results and tips to avoid making common mistakes are presented.

Identifying the estimand

Before an effect is estimated, the estimand must be specified and clarified. Although some aspects of the estimand depend not only on how the effect is estimated after weighting but also on the weighting method itself, other aspects must be considered at the time of effect estimation and interpretation. Here, we consider three aspects of the estimand: the population the effect is meant to generalize to (the target population), the effect measure, and whether the effect is marginal or conditional.

The target population. Different weighting methods allow you to estimate effects that can generalize to different target populations. The most common estimand in weighting is the average treatment effect in the population (ATE), which is the average effect of treatment for the population from which the sample is a random sample. Other common estimands include the average treatment effect in the treated (ATT), the average treatment effect in the control (ATC), and the average treatment effect in the overlap (ATO). These are defined and explained in Greifer and Stuart (2021). The estimand for weighting is controlled by the estimand argument in the call to weightit(). Other allowable estimands for some weighting methods include the average treatment effect in the matched sample (ATM) and the average treatment effect in the optimal subset (ATOS). These are treated just like the ATO and will not be differentiated further.

Marginal and conditional effects. A marginal effect is a comparison between the expected potential outcome under treatment and the expected potential outcome under control. This is the same quantity estimated in randomized trials without blocking or covariate adjustment and is particularly useful for quantifying the overall effect of a policy or population-wide intervention. A conditional effect is the comparison between the expected potential outcomes in the treatment groups within strata. This is useful for identifying the effect of a treatment for an individual patient or a subset of the population.

Effect measures. The main outcome types we consider here are continuous, with the effect measured by the mean difference; binary, with the effect measured by the risk difference (RD), risk ratio (RR), or odds ratio (OR); and time-to-event (i.e., survival), with the effect measured by the hazard ratio (HR). The RR, OR, and HR are noncollapsible effect measures, which means the marginal effect on that scale is not a (possibly) weighted average of the conditional effects within strata, even if the stratum-specific effects are of the same magnitude. For these effect measures, it is critical to distinguish between marginal and conditional effects because different statistical methods target different types of effects. The mean difference and RD are collapsible effect measures, so the same methods can be used to estimate marginal and conditional effects.

Our primary focus will be on marginal effects, which are appropriate for all effect measures, easily interpretable, and require few modeling assumptions.

G-computation

To estimate marginal effects, we use a method known as g-computation (Snowden, Rose, and Mortimer 2011) or regression estimation (Schafer and Kang 2008). This involves first specifying a model for the outcome as a function of the treatment and covariates. Then, for each unit, we compute their predicted values of the outcome setting their treatment status to treated, and then again for control, leaving us with two predicted outcome values for each unit, which are estimates of the potential outcomes under each treatment level. We compute the mean of each of the estimated potential outcomes across the entire sample, which leaves us with two average estimated potential outcomes. Finally, the contrast of these average estimated potential outcomes (e.g., their difference or ratio, depending on the effect measure desired) is the estimate of the treatment effect.

When doing g-computation after weighting, a few additional considerations are required. First, the outcome model should be fit incorporating the estimated weights (e.g., using weighted least squares or weighted maximum likelihood estimation) (Vansteelandt and Keiding 2011). Second, when we take the average of the estimated potential outcomes under each treatment level, if an estimand other than the ATT, ATC, or ATE is used or sampling weights are included, this must be a weighted average that incorporates the weights1. Third, if we want to target the ATT or ATC, we only estimate potential outcomes for the treated or control group, respectively (though we still generate predicted values under both treatment and control) (Wang, Nianogo, and Arah 2017).

G-computation as a framework for estimating effects after weighting has a number of advantages over other approaches. It works the same regardless of the form of the outcome model or type of outcome (e.g., whether a linear model is used for a continuous outcome or a logistic model is used for a binary outcome); the only difference might be how the average expected potential outcomes are contrasted in the final step. In simple cases, the estimated effect is numerically identical to effects estimated using other methods; for example, if no covariates are included in the outcome model, the g-computation estimate is equal to the difference in means from a t-test or coefficient of the treatment in a linear model for the outcome. There are analytic and bootstrap approximations to the SEs of the g-computation estimate. The analytic approximation is computed using the delta method, a technique for computing the SE of a quantity derived from the regression model parameters, such as the g-computation estimate.

For the reasons above, we use weighted g-computation when possible for all effect estimates, even if there are simpler methods that would yield the same estimates. Using a single workflow (with some slight modifications depending on the context; see below) facilitates implementing best practices regardless of what choices a user makes.

There are other methods to incorporate the outcome model into estimation of the treatment effect, the best studied of which is augmented inverse probability weighting (AIPW), which also involves a g-computation step. We only describe weighted g-computation here because of its conceptual simplicity, ease of implementation, and connection with best practices for estimating effects after matching.

Modeling the Outcome

The goal of the outcome model is to generate good predictions for use in the g-computation procedure described above. The type and form of the outcome model should depend on the outcome type. For continuous outcomes, one can use a linear model regressing the outcome on the treatment; for binary outcomes, one can use a generalized linear model with, e.g., a logistic link; for time-to-event outcomes, one can use a Cox proportional hazards model. Note that the purpose of including the outcome model is not to arrive at a doubly robust estimator (i.e., one that is consistent if either the outcome or propensity score model is correct); rather, it is simply to increase the precision of the weighted estimate essentially for free. To take advantage of this feature, it is important to use a canonical link (i.e., the default link for a given family), as recommended by Gabriel et al. (2024).

An additional decision to make is whether (and how) to include covariates in the outcome model. One may ask, why use weighting at all if you are going to model the outcome with covariates anyway? Weighting reduces the dependence of the effect estimate on correct specification of the outcome model; this is the central thesis of Ho et al. (2007) (though applied to matching in their case). Including covariates in the outcome model after weighting has several functions: it can increase precision in the effect estimate, reduce the bias due to residual imbalance, and make the effect estimate “doubly robust”, which means it is consistent if either the weighting reduces sufficient imbalance in the covariates or if the outcome model is correct. For these reasons, we recommend covariate adjustment after weighting when possible. There is some evidence that covariate adjustment is most helpful for covariates with standardized mean differences greater than .1 (Nguyen et al. 2017), so these covariates and covariates thought to be highly predictive of the outcome should be prioritized in treatment effect models if not all can be included due to sample size constraints.

Although there are many possible ways to include covariates (e.g., not just main effects but interactions, smoothing terms like splines, or other nonlinear transformations), it is important not to engage in specification search (i.e., trying many outcomes models in search of the “best” one). Doing so can invalidate results and yield a conclusion that fails to replicate. For this reason, we recommend only including the same terms included in the weighting model unless there is a strong a priori and justifiable reason to model the outcome differently.

It is important not to interpret the coefficients and tests of covariates in the outcome model. These are not causal effects and their estimates may be severely confounded. Only the treatment effect estimate can be interpreted as causal assuming the relevant assumptions about unconfoundedness are met. Inappropriately interpreting the coefficients of covariates in the outcome model is known as the Table 2 fallacy (Westreich and Greenland 2013). To avoid this, we only display the results of the g-computation procedure and do not examine or interpret the outcome models themselves.

Estimating Standard Errors and Confidence Intervals

Uncertainty estimation (i.e., of SEs, confidence intervals, and p-values) may consider the variety of sources of uncertainty present in the analysis, including (but not limited to!) estimation of the propensity score (if used) and estimation of the treatment effect (i.e., because of sampling error). For some methods, methods for analytically computing the correct asymptotic SE have been described and are implement in WeightIt when available. These methods rely on M-estimation (Stefanski and Boos 2002; Ross et al. 2024), a method of combining estimation of multiple models to adjust the standard errors for their joint estimation. For other methods, one must rely on an approximation that treats the weights as fixed and known, which can either yield conservative or anti-conservative standard errors depending on the method and estimand (Austin 2022; Reifeis and Hudgens 2020). Alternatively, one can use the bootstrap, which tends to have good performance regardless of the situation but can be computationally intensive for large datasets or slow weight estimation methods (Austin 2022). We describe the correct asymptotic standard errors, the robust standard errors treating the weights as fixed, and the bootstrap below.

Asymptotically Correct Standard Errors Using M-estimation

M-estimation involves specifying a stack of estimating equations, one for each parameter to be estimated, that are each a function of the parameters. The final parameter estimates are those that yield a vector of 0s for the solutions of the estimating equations. These estimating equations include the parameters of the weighting model (e.g., the coefficients in the logistic regression model for the propensity score) and the parameters of the outcome model (i.e., the coefficients on treatment and the covariates). It is possible to compute the joint covariance matrix of all the estimated parameters using functions of the estimating equations and the estimated parameters. We refer curious readers to Stefanski and Boos (2002) for an introduction.

Most theoretical developments on the estimation of asymptotically correct standard errors for propensity score-weighted treatment effect estimates rely on M-estimation theory (Lunceford and Davidian 2004; Reifeis and Hudgens 2020; Gabriel et al. 2024). This method can only be used when the model used to estimate the weights involves estimating equations. Currently, only weights estimated using a generalized linear model propensity score, entropy balancing, inverse probability tilting, or the just-identified covariate balancing propensity score can be accommodated with this method. glm_weightit() can be used to fit generalized linear models for the outcome that account for estimation of the weights; ordinal_weightit(), multinom_weightit(), and coxph_weightit() can be used to fit ordinal, multinomial, and Cox proportional hazards models. When estimating equations are not available for a given method, the weights are treated as fixed and known, and the M-estimation standard errors are equal to the robust standard errors described below.

Robust Standard Errors

Also known as sandwich SEs (due to the form of the formula for computing them), heteroscedasticity-consistent SEs, or Huber-White SEs, robust SEs are an adjustment to the usual maximum likelihood or ordinary least squares SEs that are robust to violations of some of the assumptions required for usual SEs to be valid (MacKinnon and White 1985). They can also be adjusted to accommodate arbitrary levels of clustering in the data (e.g., when units are sampled from schools). Robust SEs have been shown to be conservative (i.e., yielding overly large SEs and wide confidence intervals) for estimating the ATE after some forms of weighting (Robins, Hernán, and Brumback 2000), though they can be either conservative or not for other weighting methods and estimands, such as for the ATT (Reifeis and Hudgens 2020) or for entropy balancing (Chan, Yam, and Zhang 2016). Robust SEs treat the estimated weights as if they were fixed and known, ignoring uncertainty in their estimation that is otherwise accounted for by the asymptotically correct standard errors described above. Although they are quick and simple to estimate using functionality in the sandwich and survey packages or using glm_weightit(), they should be used with caution, and the bootstrap (described below) or asymptotically correct standard errors should be preferred in most cases.

Bootstrapping

Some problems with the robust SEs described above include that they fail to take into account the estimation of the weights and are only an approximation when used to compute derived quantities from nonlinear models, which is often true when using g-computation to estimate effects. One solution to these problems is bootstrapping, which is a technique used to simulate the sampling distribution of an estimator by repeatedly drawing samples with replacement and estimating the effect in each bootstrap sample (Efron and Tibshirani 1986). From the bootstrap distribution, SEs and confidence intervals can be computed in several ways, including using the standard deviation of the bootstrap estimates as the SE estimate or using the 2.5 and 97.5 percentiles as 95% confidence interval bounds. Bootstrapping tends to be most useful when no analytic estimator of a SE is possible or has been derived yet. Bootstrapping has been found to be effective at estimating SEs and confidence intervals after weighting, often performing better even than the asymptotically correct method when it is available, specially in smaller samples (Austin 2022).

Typically, bootstrapping involves performing the entire estimation process in each bootstrap sample, including estimation both of the weights and the outcome model parameters. More bootstrap replications are always better but can take time and increase the chances that at least one error will occur within the bootstrap analysis (e.g., a bootstrap sample with zero treated units or zero units with an event). In general, numbers of replications upwards of 1000 are recommended. The traditional bootstrap resamples units from the dataset, and the fractional weighted bootstrap (Xu et al. 2020) draws a weight for each unit from a specified distribution and treats the weights as sampling weights in each replication. Both are equally valid in most cases, but the fractional weighted bootstrap can outperform the traditional bootstrap when sparse categorical variables are present in the data.

Bootstrap standard errors can be computed manually using boot (for the traditional bootstrap) or fwb (for the fractional weighted bootstrap) or automatically using functionality in glm_weightit(), which, in each bootstrap sample, re-estimates the weights and then uses those weights to fit the outcome model. The covariance of the bootstrap estimates is used as the parameter variance matrix. There are some methods of computing bootstrap confidence intervals that use the bootstrap replications themselves, e.g., by using the quantiles of the bootstrap distribution. Those are not available in WeightIt and must be programmed manually.

Because bootstrap standard errors involve a random component, it is imperative to set a seed using set.seed() to ensure reproducibility.

Estimating Treatment Effects and Standard Errors After Weighting

Below, we describe effect estimation after weighting. The focus here is not on evaluating the methods but simply on demonstrating them. In all cases, the correct propensity score model is used. We will present the asymptotically correct method that uses M-estimation, the method that uses robust SEs that treat the weights as fixed, and bootstrapping.

We’ll be using a simulated toy dataset d with several outcome and treatment types. Code to generate the dataset is at the end of this document. Below we display the first six rows of d:

head(d)
A Am Ac X1 X2 X3 X4 X5 X6 X7 X8 X9 Y_C Y_B Y_S
0 C1 -2.2185 0.1725 -1.4283 -0.4103 -2.3606 1 -1.1199 0.6398 -0.4840 -0.5939 -3.591 0 857.7
0 C2 -2.2837 -1.0959 0.8463 0.2456 -0.1233 1 -2.2687 -1.4491 -0.5514 -0.3144 -1.548 0 311.6
0 C1 -1.1362 0.1768 0.7905 -0.8436 0.8237 1 -0.2221 0.2971 -0.6966 -0.6952 6.071 0 241.2
0 C1 -0.8865 -0.4595 0.1726 1.9542 -0.6266 1 -0.4019 -0.8294 -0.5384 0.2073 2.491 1 142.4
1 T 0.8613 0.3563 -1.8121 0.8135 -0.6719 1 -0.8297 1.7297 -0.6439 -0.0265 -1.100 0 206.8
0 C2 -2.1697 -2.4313 -1.7984 -1.2940 0.0461 1 -1.2419 -1.1252 -1.8659 -0.5651 -9.850 0 1962.9

A is a binary treatment variable, X1 through X9 are covariates, Y_C is a continuous outcome, Y_B is a binary outcome, and Y_S is a survival outcome. Am and Ac are multi-category and continuous treatment variables, respectively.

In addition to WeightIt and whatever package may be required to estimate the weights, we will need the following packages to perform the analyses:

library("WeightIt")
library("marginaleffects")

Effect estimates will be computed using marginaleffects::avg_comparisons(), even when its use may be superfluous (e.g., for comparing the weighted difference in means). As previously mentioned, this is because it is useful to have a single workflow that works no matter the situation, perhaps with very slight modifications to accommodate different contexts. Using avg_comparisons() has several advantages, even when the alternatives are simple: it only provides the effect estimate and not other coefficients, and it always produces average marginal effects for the correct population if requested.

Other packages may be of use but are not used here. There are alternatives to the marginaleffects package for computing average marginal effects, including margins and stdReg. The survey package can be used to estimate robust SEs incorporating weights and provides functions for survey-weighted generalized linear models and Cox-proportional hazards models. Much of the code here can be adapted to be used with survey, and we will demonstrate that as well.

The Standard Case: Binary Treatment with Asymptotically Correct SEs

For many weighting methods, estimating the effect after weighting is straightforward and involves fitting a model for the outcome that incorporates the estimated weights using lm_weightit() or glm_weightit(), then estimating the treatment effect using g-computation (i.e., using marginaleffects::avg_comparisons()). This procedure is the same for continuous and binary outcomes with and without covariates. This method uses the asymptotically correct M-estimation-based SEs when available and robust SEs otherwise or bootstrap standard errors if requested.

There are a few adjustments that need to be made for certain scenarios, which we describe in the section “Adjustments to the Standard Case”. These adjustments include for the following cases: when weighting for the ATT or ATC, for estimating effects with binary outcomes, and for estimating effects with survival outcomes. Estimation for all estimands other than the ATT and ATC proceeds as it does for the ATE. You must read the Standard Case to understand the basic procedure before reading about these special scenarios. We also demonstrate how to estimate effects for multi-category, continuous, and sequential treatments.

Here, we demonstrate the faster analytic approach to estimating confidence intervals; for the bootstrap approach, see the section “Using Bootstrapping to Estimate Confidence Intervals” below.

First, we will perform propensity score weighting for the ATE. Remember, all weighting methods use this exact procedure or a slight variation, so this section is critical even if you are using a different weighting method.

#PS weighting for the ATE with a logistic regression PS
W <- weightit(A ~ X1 + X2 + X3 + X4 + X5 + 
                X6 + X7 + X8 + X9, data = d,
              method = "glm", estimand = "ATE")
W
## A weightit object
##  - method: "glm" (propensity score weighting with GLM)
##  - number of obs.: 2000
##  - sampling weights: none
##  - treatment: 2-category
##  - estimand: ATE
##  - covariates: X1, X2, X3, X4, X5, X6, X7, X8, X9

Typically one would assess balance and ensure that this weighting specification works, but we will skip that step here to focus on effect estimation. See vignette("WeightIt") and vignette("cobalt", package = "cobalt") for more information on this necessary step.

First, we fit a model for the outcome given the treatment and (optionally) the covariates. It’s usually a good idea to include treatment-covariate interactions, which we do below, but this is not always necessary, especially when excellent balance has been achieved.

#Linear model with covariates
fit <- lm_weightit(Y_C ~ A * (X1 + X2 + X3 + X4 + X5 + 
                        X6 + X7 + X8 + X9),
                    data = d, weightit = W)

Next, we use marginaleffects::avg_comparisons() to estimate the ATE.

avg_comparisons(fit, variables = "A")
## 
##  Estimate Std. Error    z Pr(>|z|)    S 2.5 % 97.5 %
##      1.96      0.346 5.66   <0.001 26.0  1.28   2.63
## 
## Term: A
## Type:  probs 
## Comparison: 1 - 0

If, in addition to the effect estimate, we want the average estimated potential outcomes, we can use marginaleffects::avg_predictions(), which we demonstrate below. Note the interpretation of the resulting estimates as the expected potential outcomes is only valid if all covariates present in the outcome model (if any) are interacted with the treatment.

avg_predictions(fit, variables = "A")
## 
##  A Estimate Std. Error    z Pr(>|z|)    S 2.5 % 97.5 %
##  0     1.42      0.135 10.5   <0.001 84.0  1.16   1.68
##  1     3.38      0.318 10.6   <0.001 84.8  2.75   4.00
## 
## Type:  probs

We can see that the difference in potential outcome means is equal to the average treatment effect computed previously2. The arguments to avg_predictions() are the same as those to avg_comparisons().

Adjustments to the Standard Case

This section explains how the procedure might differ if any of the following special circumstances occur.

Weighting for the ATT or ATC

When weighting for the ATT, everything is identical to the Standard Case except that in the calls to avg_comparisons() and avg_predictions(), the newdata argument must additionally be supplied to avg_comparisons() and avg_predictions() as

newdata = subset(A == 1)

This requests that g-computation be done only for the treated units. For the ATC, replace 1 with 0.

Weighting for estimands other than the ATT, ATC, or ATE

When weighting for an estimand that changes the target population away from one naturally defined by the data, e.g., for the ATO, ATM, or ATOS, or when trimming weights in such a way that trimmed units are dropped from the sample (i.e., receive a weight of 0), an adjustment needs to be made to the call to avg_comparisons() and avg_predictions(). For the estimands listed, we need to supply the estimated weights to the wts argument. So, after weighting for the ATO for example, we need to specify the following:

wts = W$weights

Binary and count outcomes

Estimating effects on binary and count outcomes is essentially the same as for continuous outcomes. The main difference is that there are several measures of the effect one can consider, which include the odds ratio (OR), risk ratio/relative risk (RR), and risk difference (RD) for binary outcomes and the count/incidence ratio for count outcomes, and the syntax to avg_comparisons() depends on which one is desired. The outcome model should be one appropriate for the outcomes (e.g., logistic regression for binary outcomes or Poisson regression for count outcomes) but is unrelated to the desired effect measure because we can compute any of the above effect measures using avg_comparisons() after fitting the model.

To fit a generalized linear model, change lm_weightit() to glm_weightit() and set family = binomial for binary outcomes or family = poisson for count outcomes. To compute the marginal RD or mean difference, we can use exactly the same syntax as in the Standard Case; nothing needs to change3.

To compute the marginal RR or count/incidence ratio, first we need to add comparison = "lnratioavg" to avg_comparisons(); this computes the marginal log ratio. To get the marginal ratio itself, we need to add transform = "exp" to avg_comparisons(), which exponentiates the marginal log ratio and its confidence interval. The code below computes the effects and displays the statistics of interest for a binary outcome Y_B:

#Logistic regression model with covariates
fit <- glm_weightit(Y_B ~ A * (X1 + X2 + X3 + X4 + X5 + 
                        X6 + X7 + X8 + X9),
                    data = d, weightit = W,
                    family = binomial)

#Compute effects; RR and confidence interval
avg_comparisons(fit,
                variables = "A",
                comparison = "lnratioavg",
                transform = "exp")
## 
##  Estimate Pr(>|z|)    S 2.5 % 97.5 %
##      1.64   <0.001 39.5  1.43   1.88
## 
## Term: A
## Type:  probs 
## Comparison: ln(mean(1) / mean(0))

The output displays the marginal RR, its Z-value, the p-value for the Z-test of the log RR against 0, and its confidence interval. (Note that even though the Comparison label still suggests the log RR, the RR is actually displayed.) To view the log RR and its standard error, omit the transform argument.

For the marginal OR, the only thing that needs to change is that comparison should be set to "lnoravg".

Multi-category outcomes can be modeled using ordinal_weightit() and multinom_weightit(), which fit ordinal and multinomial regression models, respectively.

Survival outcomes

There are several measures of effect size for survival outcomes, some of which are described by Mao et al. (2018). When using the Cox proportional hazards model, the quantity of interest is the hazard ratio (HR) between the treated and control groups. As with the OR, the HR is non-collapsible, which means the estimated HR will only be a valid estimate of the marginal HR when no other covariates are included in the model. Other effect measures, such as the difference in mean survival times or probability of survival after a given time, can be treated just like continuous and binary outcomes as previously described.

For the HR, we cannot compute average marginal effects and must use the coefficient on treatment in a Cox model fit without covariates. This means that we cannot use the procedures from the Standard Case. Here we describe estimating the marginal HR using coxph_weightit(), which adapts coxph() from the survival package but allows for estimation of SEs that account for estimation of the weights. Robust SEs for HRs were studied by Austin (2016) and were found to be conservative. Other formulas have been developed for estimating standard errors more accurately (Mao et al. 2018; Hajage et al. 2018), though Austin (2016) also found the bootstrap to be adequate. The HC0 robust standard error is requested by default, and bootstrap standard errors can be requested by setting vcov to "BS" or "FWB".

#Cox Regression for marginal HR
fit <- coxph_weightit(survival::Surv(Y_S) ~ A, data = d,
                      weightit = W)

#Log HR estimates
summary(fit)
## 
## Call:
## coxph_weightit(formula = survival::Surv(Y_S) ~ A, data = d, weightit = W)
## 
## Coefficients:
##   Estimate Std. Error z value Pr(>|z|)    
## A   0.3873     0.0965    4.02  5.9e-05 ***
## Standard error: HC0 robust

Note that the output differs from that of survival::coxph() and is more similar to that of glm(). The Estimate column contains the log HR. We can request the HR by setting transform = "exp" in the call to summary().

#HR and CIs; requested by exponentiating log HRs
summary(fit, ci = TRUE, transform = "exp")
## 
## Call:
## coxph_weightit(formula = survival::Surv(Y_S) ~ A, data = d, weightit = W)
## 
## Coefficients (transformed):
##   Estimate z value Pr(>|z|) 2.5 % 97.5 %    
## A     1.47    4.02  5.9e-05  1.22   1.78 ***
## Standard error: HC0 robust

The adjustedCurves package provides integration with WeightIt to estimate adjusted survival estimands. We strongly recommend using this package to estimate effects after weighting.

Using sampling weights and/or clustered data

When sampling weights are required to generalize to the correct population, they must be included in the call to weightit() to estimate the balancing weights, in the outcome model, and in the call to avg_comparisons(), etc. In most cases, this can be handled without modifying the standard case except that s.weights must be supplied to weightit(), and wts = W$s.weights must be supplied to avg_comparisons(), etc. lm_weightit() and glm_weightit() automatically incorporate the sampling weights into estimation of the outcome model.

If there is clustering in the sampling design, such clustering can be accounted for by using the cluster argument to glm_weightit(), which accepts a one-sided formula with the clustering variable(s) on the right side. These can be used with the traditional or fractional weighted bootstrap as well.

#Estimate the balancing weights, with sampling weights called "sw"
W <- weightit(A ~ X1 + X2 + X3 + X4 + X5 + 
                X6 + X7 + X8 + X9, data = d,
              method = "glm", estimand = "ATE",
              s.weights = "sw")

#Fit the outcome model, with clustering variable "clu"
fit <- glm_weightit(Y_C ~ A * (X1 + X2 + X3 + X4 + X5 + 
                           X6 + X7 + X8 + X9),
                    data = d, weightit = W,
                    cluster = ~clu)

#Compute the ATE, include sampling weights in the estimation
avg_comparisons(fit,
                variables = "A",
                wts = W$s.weights)

More complicated survey designs might require the use of the survey package to handle them; it is important to note that using survey::svyglm() instead of glm_weightit() will produce standard errors that do not account for estimation of the balancing weights. See an example below for using survey to estimate effects after weighting.

library(survey)

#Multiply sampling weights and estimated weights
d$weights <- W$weights * d$sw

#Declare a survey design using the combined weights with
#appropriate clustering
des <- svydesign(~clu, weights = ~weights, data = d)

#Fit the outcome model
fit <- svyglm(Y_C ~ A * (X1 + X2 + X3 + X4 + X5 + 
                           X6 + X7 + X8 + X9),
              design = des)

#G-computation for the difference in means, including sampling weights
avg_comparisons(fit,
                variables = "A",
                wts = d$sw)

Multi-Category Treatments

Multi-category treatments work essentially the same way as binary treatments. The main practical differences are in choosing the estimand and estimating the weights. The ATE and ATO are straightforward. The ATT requires choosing one group to be the treated or “focal” group. Effects are the estimated for members of that group. The contrast in the focal group between the expected potential outcomes under a non-focal treatment and the expected potential outcomes for the focal (actual) treatment can be interpreted similarly to how ATTs are interpreted for binary treatments. The contrasts in the focal group between expected potential outcomes under a pair of non-focal treatments can be interpreted as the contrast between the ATTs of the non-focal treatments. Weights may be estimated differently for multi-category treatments from binary treatments; see the individual methods pages for how they differ.

Below, we’ll estimate the ATTs of the multi-category treatment Am for a focal level.

table(d$Am)
## 
##  C1  C2   T 
## 751 801 448

We have a focal treatment group, "T", and two control groups "C1" and "C2". We expect the ATTs for the two control groups to be the same since we assigned them randomly from within the original control group.

First, we estimate our weights using entropy balancing (Hainmueller 2012), identifying the focal group using focal:

W <- weightit(Am ~ X1 + X2 + X3 + X4 + X5 + 
                X6 + X7 + X8 + X9, data = d,
              method = "ebal", estimand = "ATT",
              focal = "T")
W
## A weightit object
##  - method: "ebal" (entropy balancing)
##  - number of obs.: 2000
##  - sampling weights: none
##  - treatment: 3-category (C1, C2, T)
##  - estimand: ATT (focal: T)
##  - covariates: X1, X2, X3, X4, X5, X6, X7, X8, X9

Typically one would assess the performance of the weights (balance and effective sample size) but we will skip that for now. Next, we fit the outcome model and perform weighted g-computation. We use avg_predictions() first to compute the expected potential outcome under each treatment for the focal group, and the use hypotheses() to test all pairwise comparisons.

#Fit the outcome model
fit <- lm_weightit(Y_C ~ Am * (X1 + X2 + X3 + X4 + X5 + 
                                 X6 + X7 + X8 + X9),
                   data = d, weightit = W)

#G-computation
p <- avg_predictions(fit,
                     variables = "Am",
                     newdata = subset(Am == "T"))
p

hypotheses(p, ~pairwise)

We find significant ATTs between the focal treatment and control levels (T - C1 and T - C2), but the difference between control levels (C2 - C1), which can be interpreted as the difference between these ATTs, is nonsignificant, as expected.

Continuous Treatments

For continuous treatments, one estimand of interest is the average dose-response function (ADRF), which links the value of the treatment to the expected potential outcome under that treatment value across the full sample. We do not provide a detailed account of the ADRF but will demonstrate how to estimate weights that balance covariates with respect to a continuous treatment and how to estimate and plot the ADRF in the weighted sample. We’ll use the continuous treatment Ac, which ranges from -10.37 to 6.26, and estimate its effect on the continuous outcome Y_C.

First, we’ll estimate entropy balancing weights (Vegetabile et al. 2021) using weightit().

W <- weightit(Ac ~ X1 + X2 + X3 + X4 + X5 + 
                X6 + X7 + X8 + X9, data = d,
              moments = 2, int = TRUE,
              method = "ebal")
W
## A weightit object
##  - method: "ebal" (entropy balancing)
##  - number of obs.: 2000
##  - sampling weights: none
##  - treatment: continuous
##  - covariates: X1, X2, X3, X4, X5, X6, X7, X8, X9

Typically one would assess the performance of the weights (balance and effective sample size) but we will skip that for now. Next, we fit the outcome model and perform weighted g-computation. For the outcome model, we will use a natural cubic spline on Ac with 4 df. You can use any transformation of the treatment, e.g., a polynomial transformation using poly(), as long as it is flexible enough to capture any possible ADRF; purely nonparametric methods like kernel regression can be used as well, but inference for them is more challenging. Covariates can be included in the model, but with many covariates, many basis functions for the treatment, and a full set of treatment-covariate interactions, the resulting estimates may be imprecise.

#Fit the outcome model
fit <- lm_weightit(Y_C ~ splines::ns(Ac, df = 4) *
                     (X1 + X2 + X3 + X4 + X5 + 
                        X6 + X7 + X8 + X9),
                   data = d, weightit = W)

Next we use avg_predictions() first to compute the expected potential outcome under a representative set of treatment values. We’ll examine 31 treatment values from the 10th to 90th percentiles of Ac because estimates outside those ranges tend to be imprecise.

#Represenative values of Ac:
values <- with(d, seq(quantile(Ac, .1),
                      quantile(Ac, .9),
                      length.out = 31))

#G-computation
p <- avg_predictions(fit,
                     variables = list(Ac = values))

Although one can examine the expected potential outcomes, it is often more useful to see them plotted. We can generate a plot of the ADRF and its pointwise confidence band using ggplot24:

library("ggplot2")
ggplot(p, aes(x = Ac)) +
  geom_line(aes(y = estimate)) +
  geom_ribbon(aes(ymin = conf.low, ymax = conf.high),
              alpha = .3) +
  labs(x = "Ac", y = "E[Y|Ac]") +
  theme_bw()

We can see that the ADRF has an elbow-shape, with a zone of no evidence of treatment effect followed by a zone of increasing values of the outcome as the treatment increases.

Another way to characterize the effect of continuous treatments is to examine the average marginal effect function (AMEF), which is a function that relates the value of treatment to the derivative of the ADRF. When this derivative is different from zero, there is evidence of a treatment effect at the corresponding value of treatment. Below, we use avg_slopes() to compute the pointwise derivatives of the ADRF across levels of Ac and then plot it5.

# Estimate the pointwise derivatives at representative
# values of Ac
s <- avg_slopes(fit,
                variables = "Ac",
                newdata = datagrid(Ac = values,
                                   grid_type = "counterfactual"),
                by = "Ac")

# Plot the AMEF
ggplot(s, aes(x = Ac)) +
  geom_line(aes(y = estimate)) +
  geom_ribbon(aes(ymin = conf.low, ymax = conf.high),
              alpha = .3) +
  geom_hline(yintercept = 0, linetype = "dashed") +
  labs(x = "Ac", y = "dE[Y|Ac]/dAc") +
  theme_bw()

We can see that between values of -1.8 and .5, there is evidence of a positive effect of Ac on Y_C (i.e., because the confidence intervals for the slope of the ADRF at those values of Ac exclude 0). This is in line with our observation above that the treatment only appears to have an effect at higher values of Ac.

Longitudinal Treatments

For longitudinal treatments, estimation proceeds as usual, except that we estimate weights using weightitMSM() and fit a weighted outcome model that includes treatment at all time periods (and, optionally, and covariates measured prior to the first treatment). It is important not to include any covariates possibly caused by any of the treatments to avoid any bias; this is the whole point of using weighting to estimate the marginal structural model in the first place.

glm_weightit() makes it easy to incorporate the weights and account for their estimation in computing the parameter variance matrix, if desired. As with other methods, when the components required for M-estimation are included in the weightitMSM() output, they will be used to estimate standard errors that adjust for estimation of the weights. Otherwise, the weights can be treated as fixed or the whole estimation can be done through bootstrapping.

Below, we demonstrate using the usual inverse probability weights for a marginal structural model. Because these weights are computed from propensity scores estimated with logistic regression, we can use M-estimation to adjust for their estimation in computing the parameter covariance matrix. This also includes estimation of the standardization factor, if any. We’ll use the toy dataset msmdata that comes with WeightIt.

data("msmdata")

Wmsm <- weightitMSM(list(A_1 ~ X1_0 + X2_0,
                             A_2 ~ X1_1 + X2_1 +
                               A_1 + X1_0 + X2_0,
                             A_3 ~ X1_2 + X2_2 +
                               A_2 + X1_1 + X2_1 +
                               A_1 + X1_0 + X2_0),
                        data = msmdata, method = "glm",
                        stabilize = TRUE)

Next we’ll fit the outcome model using glm_weightit(), which includes the baseline covariates (i.e., only those measured prior the first treatment).

fit <- glm_weightit(Y_B ~ A_1 * A_2 * A_3 * (X1_0 + X2_0),
                    data = msmdata, weightit = Wmsm,
                    family = binomial)

Then, we compute the average expected potential outcomes under each treatment regime using marginaleffects::avg_predictions():

p <- avg_predictions(fit,
                     variables = c("A_1", "A_2", "A_3"))

p
## 
##  A_1 A_2 A_3 Estimate Std. Error    z Pr(>|z|)     S 2.5 % 97.5 %
##    0   0   0    0.687     0.0166 41.4   <0.001   Inf 0.654  0.719
##    0   0   1    0.521     0.0379 13.7   <0.001 140.3 0.447  0.595
##    0   1   0    0.491     0.0213 23.1   <0.001 389.1 0.449  0.532
##    0   1   1    0.438     0.0295 14.8   <0.001 163.2 0.380  0.496
##    1   0   0    0.602     0.0211 28.5   <0.001 590.8 0.561  0.644
##    1   0   1    0.544     0.0314 17.3   <0.001 221.0 0.482  0.605
##    1   1   0    0.378     0.0163 23.2   <0.001 393.1 0.346  0.410
##    1   1   1    0.422     0.0261 16.1   <0.001 192.3 0.371  0.473
## 
## Type:  probs

We can compare individual predictions using marginaleffects::hypotheses(). For example, to compare all treatment histories to just the first treatment history (i.e., in which all units are untreated for all time periods), we can run the following:

hypotheses(p, ~reference)

Moderation Analysis

Moderation analysis involves determining whether a treatment effect differs across levels of another variable. The use of weighting with moderation analysis is described in Griffin et al. (2023). The goal is to achieve balance within each subgroup of the potential moderating variable, and there are several ways of doing so. Broadly, one can either perform weighting in the full dataset and interact the moderator with the other covariates in the weighting model, or one can perform completely separate analyses in each subgroup.

In some cases, e.g., when using unregularized parametric methods like GLM propensity score weighting, CBPS, entropy balancing, or inverse probability tilting, both methods yield identical results. To fit a weighting model within each subgroup, one can use the by argument to weightit(), which does just this6. There is also the possibility of using the subgroup balancing propensity score (Dong et al. 2020) implemented using sbps(), which determines whether a single weighting model fit to the whole or a subgroup-specific weighting model is best for each subgroup and overall. The chosen approach should be that which achieves the best balance, though we don’t demonstrate assessing balance here to maintain focus on effect estimation.

We’ll consider the binary variable X5 to be the potential moderator of the effect of A on Y_C. Below, we’ll estimate weights using CBPS for the ATE within each level of X5 by supplying it to the by argument.

Wm <- weightit(A ~ X1 + X2 + X3 + X4 + X5 + 
                 X6 + X7 + X8 + X9, data = d,
               method = "cbps", estimand = "ATE",
               by = ~X5)

Wm
## A weightit object
##  - method: "cbps" (covariate balancing propensity score weighting)
##  - number of obs.: 2000
##  - sampling weights: none
##  - treatment: 2-category
##  - estimand: ATE
##  - covariates: X1, X2, X3, X4, X5, X6, X7, X8, X9
##  - by: X5

Because we are using CBPS, equivalent weights could be estimated by interacting X5 with the other covariates, e.g., A ~ X5 * (X1 + X2 + ...).

It is straightforward to check subgroup balance using cobalt::bal.tab(), which has a cluster argument that can be used to assess balance within subgroups, e.g., by cobalt::bal.tab(Wm, cluster = "X5"). See vignette("segmented-data", package = "cobalt") for details.

If we are satisfied with balance, we can then model the outcome with an interaction between the treatment, the moderator, and, optionally, the covariates. This is equivalent to fitting a model for the outcome within each combination of the treatment and moderator, so it can be a good idea to simplify this model to include just the most important covariates to the outcome in order to avoid estimating too many parameters. At a minimum, you must include an interaction between the treatment and moderator. Here, we’ll fit a linear regression model including the just the first three covariates in addition to the treatment and moderator (allowing the covariates to interact with both).

fit <- lm_weightit(Y_C ~ A * X5 * (X1 + X2 + X3),
                    data = d, weightit = Wm)

To estimate the subgroup ATEs, we can use avg_comparisons(), this time specifying the by argument to signify that we want treatment effects stratified by the moderator.

a <- avg_comparisons(fit, variables = "A",
                     by = "X5")
a
## 
##  X5 Estimate Std. Error    z Pr(>|z|)    S 2.5 % 97.5 %
##   0     1.16      0.480 2.41   0.0159  6.0 0.217   2.10
##   1     2.38      0.454 5.24   <0.001 22.5 1.487   3.27
## 
## Term: A
## Type:  probs 
## Comparison: 1 - 0

We can see that the subgroup mean differences differ from each other, and we can formally test for moderation using hypotheses():

hypotheses(a, ~pairwise)

Though the subgroup effects differ from each other in the sample, this difference is not statistically significant at the .05 level, so there is no evidence of moderation by X5.

When the moderator has more than two levels, it is possible to run an omnibus test for moderation by changing hypothesis to ~reference and supplying the output to hypotheses() with joint = TRUE, e.g.,

avg_comparisons(fit, variables = "A",
                by = "X5",
                hypothesis = ~reference) |>
  hypotheses(joint = TRUE)

This produces a single p-value for the test that all pairwise differences between subgroups are equal to zero.

It is important to remember that the presence of moderation does not imply that the moderator causes differences in the treatment effect; it may simply be associated with a variable that interacts with the treatment (VanderWeele 2009). In addition, depending on which variables are adjusted for and how, moderation may not necessarily represent a disparity, even if one group experiences less benefit than another group (Jackson 2021).

Reporting Results

It is important to be as thorough and complete as possible when describing the methods of estimating the treatment effect and the results of the analysis. This improves transparency and replicability of the analysis. Results should at least include the following:

All this is in addition to information about the weighting method, estimand, propensity score estimation procedure (if used), balance assessment, etc.

References

Austin, Peter C. 2016. “Variance Estimation When Using Inverse Probability of Treatment Weighting (IPTW) with Survival Analysis.” Statistics in Medicine 35 (30): 5642–55. https://doi.org/10.1002/sim.7084.
———. 2022. “Bootstrap Vs Asymptotic Variance Estimation When Using Propensity Score Weighting with Continuous and Binary Outcomes.” Statistics in Medicine 41 (22): 4426–43. https://doi.org/10.1002/sim.9519.
Chan, Kwun Chuen Gary, Sheung Chi Phillip Yam, and Zheng Zhang. 2016. “Globally Efficient Non-Parametric Inference of Average Treatment Effects by Empirical Balancing Calibration Weighting.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 78 (3): 673–700. https://doi.org/10.1111/rssb.12129.
Dong, Jing, Junni L Zhang, Shuxi Zeng, and Fan Li. 2020. “Subgroup Balancing Propensity Score.” Statistical Methods in Medical Research 29 (3): 659–76. https://doi.org/10.1177/0962280219870836.
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Gabriel, Erin E., Michael C. Sachs, Torben Martinussen, Ingeborg Waernbaum, Els Goetghebeur, Stijn Vansteelandt, and Arvid Sjölander. 2024. “Inverse Probability of Treatment Weighting with Generalized Linear Outcome Models for Doubly Robust Estimation.” Statistics in Medicine 43 (3): 534–47. https://doi.org/10.1002/sim.9969.
Greifer, Noah, and Elizabeth A. Stuart. 2021. “Choosing the Estimand When Matching or Weighting in Observational Studies.” arXiv:2106.10577 [Stat], June. https://arxiv.org/abs/2106.10577.
Griffin, Beth Ann, Megan S. Schuler, Matt Cefalu, Lynsay Ayer, Mark Godley, Noah Greifer, Donna L. Coffman, and Daniel F. McCaffrey. 2023. “A Tutorial for Propensity Score Weighting for Moderation Analysis With Categorical Variables: An Application Examining Smoking Disparities Among Sexual Minority Adults.” Medical Care 61 (12): 836–45. https://doi.org/10.1097/MLR.0000000000001922.
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Jackson, John W. 2021. “Meaningful Causal Decompositions in Health Equity Research: Definition, Identification, and Estimation Through a Weighting Framework.” Epidemiology 32 (2): 282. https://doi.org/10.1097/EDE.0000000000001319.
Lunceford, Jared K., and Marie Davidian. 2004. “Stratification and Weighting via the Propensity Score in Estimation of Causal Treatment Effects: A Comparative Study.” Statistics in Medicine 23 (19): 29372960. https://doi.org/10.1002/sim.1903.
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Mao, Huzhang, Liang Li, Wei Yang, and Yu Shen. 2018. “On the Propensity Score Weighting Analysis with Survival Outcome: Estimands, Estimation, and Inference.” Statistics in Medicine 37 (26): 3745–63. https://doi.org/10.1002/sim.7839.
Nguyen, Tri-Long, Gary S. Collins, Jessica Spence, Jean-Pierre Daurès, P. J. Devereaux, Paul Landais, and Yannick Le Manach. 2017. “Double-Adjustment in Propensity Score Matching Analysis: Choosing a Threshold for Considering Residual Imbalance.” BMC Medical Research Methodology 17: 78. https://doi.org/10.1186/s12874-017-0338-0.
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Vegetabile, Brian G., Beth Ann Griffin, Donna L. Coffman, Matthew Cefalu, Michael W. Robbins, and Daniel F. McCaffrey. 2021. “Nonparametric Estimation of Population Average Dose-Response Curves Using Entropy Balancing Weights for Continuous Exposures.” Health Services and Outcomes Research Methodology 21 (1): 69–110. https://doi.org/10.1007/s10742-020-00236-2.
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Code to Generate Data used in Examples

#Generating data similar to Austin (2009) for demonstrating treatment effect estimation
gen_X <- function(n) {
  X <- matrix(rnorm(9 * n), nrow = n, ncol = 9)
  X[,5] <- as.numeric(X[,5] < .5)
  X
}

gen_Ac <- function(X) {
  LP_A <- -1.2 + log(2)*X[,1] - log(1.5)*X[,2] + log(2)*X[,4] - log(2.4)*X[,5] + log(2)*X[,7] - log(1.5)*X[,8]
  LP_A + rlogis(nrow(X))
}

#~20% treated
gen_A <- function(Ac) {
  1 * (Ac > 0)
}

gen_Am <- function(A) {
  factor(ifelse(A == 1, "T", sample(c("C1", "C2"), length(A), TRUE)))
}

# Continuous outcome
gen_Y_C <- function(A, X) {
  2*A + 2*X[,1] + 2*X[,2] + 2*X[,3] + 1*X[,4] + 2*X[,5] + 1*X[,6] + rnorm(length(A), 0, 5)
}
#Conditional:
#  MD: 2
#Marginal:
#  MD: 2

# Binary outcome
gen_Y_B <- function(A, X) {
  LP_B <- -2 + log(2.4)*A + log(2)*X[,1] + log(2)*X[,2] + log(2)*X[,3] + log(1.5)*X[,4] + log(2.4)*X[,5] + log(1.5)*X[,6]
  P_B <- plogis(LP_B)
  rbinom(length(A), 1, P_B)
}
#Conditional:
#  OR:   2.4
#  logOR: .875
#Marginal:
#  RD:    .144
#  RR:   1.54
#  logRR: .433
#  OR:   1.92
#  logOR  .655

# Survival outcome
gen_Y_S <- function(A, X) {
  LP_S <- -2 + log(2.4)*A + log(2)*X[,1] + log(2)*X[,2] + log(2)*X[,3] + log(1.5)*X[,4] + log(2.4)*X[,5] + log(1.5)*X[,6]
  sqrt(-log(runif(length(A)))*2e4*exp(-LP_S))
}
#Conditional:
#  HR:   2.4
#  logHR: .875
#Marginal:
#  HR:   1.57
#  logHR: .452

set.seed(19599)

n <- 2000
X <- gen_X(n)
Ac <- gen_Ac(X)
A <- gen_A(Ac)
Am <- gen_Am(A)

Y_C <- gen_Y_C(A, X)
Y_B <- gen_Y_B(A, X)
Y_S <- gen_Y_S(A, X)

d <- data.frame(A, Am, Ac, X, Y_C, Y_B, Y_S)

  1. Note: Previously, this guide recommended always including the weights. We believe best practice is to omit them, though this does not mean previous analyses using weighted averages are invalid.↩︎

  2. To verify that they are equal, supply the output of avg_predictions() to hypotheses(), e.g., avg_predictions(...) |> hypotheses(~pairwise); this explicitly compares the average potential outcomes and should yield identical estimates to the avg_comparisons() call.↩︎

  3. Note that for low or high average expected risks computed with predictions(), the confidence intervals may go below 0 or above 1; this is because an approximation is used. To avoid this problem, bootstrapping or simulation-based inference can be used instead.↩︎

  4. You can also use marginaleffects::plot_predictions(), though after requesting the predictions in the prior step it is quicker to use ggplot().↩︎

  5. You can also use plot_slopes()↩︎

  6. Currently, when by is used, M-estimation based standard errors cannot be computed, but all methods compatible with M-estimation yield identical results when using by vs. interacting the moderator with the other covariates in the weighting model. Here, we demonstrate using by because it is more generalizable to other methods.↩︎