help treatrew

Title

treatrew - Estimation of Average Treatment Effects by reweighting on propensity score

Syntax

treatrew
outcome
treatment
[varlist]
[if] [in]
[weight],
model(modeltype)
[GRaphic
conf(number)
vce(robust)]

fweights, iweights, and pweights are allowed;
see weight.

Description

treatrew estimates Average Treatment Effects by reweighting on propensity score
as proposed by Rosenbaum and Rubin (1983) in their seminal article.
Depending on the model specified, treatrew provides consistent estimation of Average Treatment
Effects under the hypothesis of "selection on observables".
Conditional on a pre-specified set of observable exogenous variables x - thought of as those driving
the non-random assignment to treatment - treatrew estimates the Average Treatment Effect (ATE),
the Average Treatment Effect on Treated (ATET) and the Average Treatment Effect on Non-Treated (ATENT),
as well as the estimates of these parameters conditional on the observable factors x (i.e., ATE(x), ATET(x) and ATENT(x)).
Parameters standard errors are provided either analytically (following Wooldridge, 2010, p. 920-930) and via bootstrapping.
treatrew assumes that the propensity score specification is correct.

According to the syntax:

outcome: is the target variable over which measuring the impact of the treatment

treatment: is the binary treatment variable taking 1 for treated, and 0 for untreated units

varlist: is the set of pre-treatment (or observable confounding) variables

Options

model(modeltype) specifies the model
for estimating the propensity score, where modeltype must be one out of these two:
"probit" or "logit". It is always required to specify one model.

graphic allows for a graphical representation of the density distributions of
ATE(x), ATET(x) and ATENT(x).

vce(robust) allows for robust regression standard errors in the probit or logit estimates.

conf(number) sets the confidence level of probit or logit estimates equal to the specified number.
The default is number=95.

modeltype_options

Model
probit The propensity score is estimated by a probit regression

logit The propensity score is estimated by a logit regression

treatrew creates a number of variables:

ATE_x is an estimate of the idiosyncratic Average Treatment Effect.

ATET_x is an estimate of the idiosyncratic Average Treatment Effect on treated.

ATENT_x is an estimate of the idiosyncratic Average Treatment Effect on Non-Treated.

treatrew returns the following scalars:

e(N) is the total number of (used) observations.

e(N1) is the number of (used) treated units.

e(N0) is the number of (used) untreated units.

e(ate) is the value of the Average Treatment Effect.

e(atet) is the value of the Average Treatment Effect on Treated.

e(atent) is the value of the Average Treatment Effect on Non-treated.

Remarks

The treatment has to be a 0/1 binary variable (1 = treated, 0 = untreated).

It is assumed that the probit or logit model is correctly specified.

Please remember to use the update query command before running
this program to make sure you have an up-to-date version of Stata installed.

Examples

*** EXAMPLE ON "JTRAIN2.DTA" ***

. #delimit ;
. xi: treatrew re78 train educ black re75 unem74 unem78 lre74 agesq mosinex ,
. vce(robust) conf(90) model(probit) gr
. ;

. #delimit ;
. xi: treatrew re78 train educ black re75 unem74 unem78 lre74 agesq mosinex ,
. model(logit) vce(robust) conf(90) gr
. ;

*** EXAMPLE ON HOW TO BOOTSTRAP STD. ERR. FOR "ATET" AND "ATENT" ***

. #delimit ;
. xi: bootstrap ate=e(ate) atet=e(atet) atent=e(atent), rep(10):
. treatrew re78 train educ black re75 unem74 unem78 lre74 agesq mosinex ,
. model(logit) vce(robust) conf(90) gr
. ;

References

Cameron, A.C., and P.K. Trivedi. 2005. Microeconometrics: Methods and Applications.
Chapter 25. Cambridge University Press, New York.

Cerulli, G. 2012. Ivtreatreg: a new STATA routine for estimating binary treatment models with heterogeneous
response to treatment under observable and unobservable selection,
Working Paper Cnr-Ceris, N° 03/2012.

Rosenbaum, P., and D.B. Rubin. 1983. The Central Role of the Propensity Score in Observational Studies for Causal Effects. Biometrika, 70, 41-55.

Wooldridge, J.M. 2002. Econometric Analysis of Cross Section and Panel Data.
Chapter 18. The MIT Press, Cambridge.

Wooldridge, J.M. 2010. Econometric Analysis of Cross Section and Panel Data, 2nd Edition.
Chapter 21. The MIT Press, Cambridge.

Acknowledgments

I wish to thank Enrico Viarisio of the Ceris-CNR technical staff for his help in
formatting the Technical Report accompanying this routine.

Author

Giovanni Cerulli

Ceris-CNR

Institute for Economic Research on Firms and Growth, National Research Council of Italy

E-mail: g.cerulli@ceris.cnr.it

Also see

Online: treatreg, ivregress, ivtreatreg, pscore, psmatch2, nnmatch