multe {multe} | R Documentation |
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")