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 ORCID iD [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_score missing

race

Race

Source

doi:10.3886/E112609V1

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 lm function.

treatment_name

name of treatment variable

cluster

Factor variable that defines clusters. If NULL (or not supplied), the command computes heteroscedasticity-robust standard errors, rather than cluster-robust standard errors.

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 FALSE), or should it draw from the marginal empirical treatment distribution (if TRUE)?

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")