| Title: | Multiple Treatment Effects Regression |
| Version: | 1.1.0 |
| Description: | Implements contamination bias diagnostics and alternative estimators for regressions with multiple treatments. The implementation is based on Goldsmith-Pinkham, Hull, and Kolesár (2024) <doi:10.48550/arXiv.2106.05024>. |
| Depends: | R (≥ 4.3.0) |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| LazyData: | true |
| Imports: | stats, nnet |
| Suggests: | spelling, knitr, formatR, rmarkdown, testthat (≥ 3.0.0) |
| Config/testthat/edition: | 3 |
| Language: | en-US |
| URL: | https://github.com/kolesarm/multe |
| BugReports: | https://github.com/kolesarm/multe/issues |
| RoxygenNote: | 7.3.2 |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2024-07-12 15:47:07 UTC; kolesarm |
| Author: | Michal Kolesár 
[aut, cre],
Paul Goldsmith-Pinkham [ctb],
Peter Hull [ctb] |
| Maintainer: | Michal Kolesár <kolesarmi@googlemail.com> |
| Repository: | CRAN |
| Date/Publication: | 2024-07-12 16:00:02 UTC |
ECLS data from Fryer and Levitt (2013)
Description
This dataset contains a subset of the publicly available Early Childhood Longitudinal Study Birth Cohort data from Fryer and Levitt (2013).
Usage
fl
Format
A data frame with 8806 rows corresponding to children and 21 columns corresponding to the variables:
- W1C0
Sampling weights (first interview)
- W2C0
Sampling weights (second interview)
- multiple_birth
Multiple birth status
- parent_score
Interviewer rating of the effectiveness of the 'parent as a teacher', Nursing Child Assessment Teaching Scale (total score).
- SES_quintile
Quintile of socioeconomic status
- region
US region
- interviewer_ID_9
Interviewer ID (first interview)
- interviewer_ID_24
Interviewer ID (second interview)
- mom_age
Age of mother
- days_premature
Days premature
- siblings
Number of siblings
- family_structure
Family structure
- birthweight
Birthweight category
- female
Female
- mom_age_NA
Age of mother missing
- age_9
Age at first interview
- age_24
Age at second interview
- std_iq_9
Standardized IQ at first interview
- std_iq_24
Standardized IQ at second interview
- parent_score_NA
parent_scoremissing- race
Race
Source
References
Roland G Fryer and Steven D Levitt. Testing for racial differences in the mental ability of young children. American Economic Review, 103(2):981–1005, April 2013. doi:10.1093/qje/qjy006
Multiple Treatment Effects Regression
Description
Compute contamination bias diagnostics for the partially linear (PL) regression estimator with multiple treatments. Also report four alternative estimators:
- OWN
The own treatment effect component of the PL estimator.
- ATE
The unweighted average treatment effect, implemented using interacted regression.
- EW
Weighted ATE estimator based on easiest-to-estimate weighting (EW) scheme, implemented by running one-treatment-at-a-time regressions.
- CW
Weighted ATE estimator using easiest-to-estimate common weighting (CW) scheme, implemented using weighted regression.
Usage
multe(r, treatment_name, cluster = NULL, tol = 1e-07, cw_uniform = FALSE)
Arguments
r |
Fitted model, output of the |
treatment_name |
name of treatment variable |
cluster |
Factor variable that defines clusters. If |
tol |
Numerical tolerance for computing LM test statistic for testing variability of the propensity score. |
cw_uniform |
For the CW estimator, should the target weighting scheme
give all comparisons equal weight (if |
Value
Returns a list with the following components:
- est_f
Data frame with alternative estimators and standard errors for the full sample
- est_o
Data frame with alternative estimators and standard errors for the overlap sample
- cb_f, cb_0
Data frame with differences between PL and alternative estimators, along with standard errors for the full, and for the overlap sample.
- n_f, n_o
Sample sizes for the full, and for the overlap sample.
- k_f, k_o
Number of controls for the full, and for the overlap sample.
- t_f, t_o
LM and Wald statistic, degrees of freedom, and p-values for the full and for the overlap sample, for testing the hypothesis of no variation in the propensity scores.
- pscore_sd_f, pscore_sd_o
Standard deviation of the estimated propensity score in the full and overlap samples.
- Y, X, wgt
Vector of outcomes, treatments and weights in the overlap sample
- Zm
Matrix of controls in the overlap sample
References
Paul Goldsmith-Pinkham, Peter Hull, and Michal Kolesár. Contamination bias in linear regressions. ArXiv:2106.05024, February 2024.
Examples
wbh <- fl[fl$race=="White" | fl$race=="Black" | fl$race=="Hispanic", ]
wbh <- droplevels(wbh)
r1 <- stats::lm(std_iq_24~race+factor(age_24)+female, weight=W2C0, data=wbh)
m1 <- multe(r1, treatment="race")