*! version 0.9.3 04aug2025
psr -- Propensity score residual regression for overlap-weight average treatment effect

Syntax

psr
depvar
treatment_var covariates [if] [in]
[, options]

psr
depvar
(
treatment_var
=
instrument
)
covariates [if] [in]
[, options]

Model
logit specifies to use logit instead of probit for
propensity/instrument score regression.

order(#) specifies the maximum polynomial order q for
predicting the dependent variable by the fitted linear index or the
fitted probability. The default value is 2.

useprob uses the fitted probability for the prediction
of the dependent variable instead of fitted linear index. The linear
index is used, if this option is not called for.

Reporting
auxiliary reports results from auxiliary regression
for covariate slopes with the standard errors.

verbose displays all intermediate step results.

vverbose is the same as auxiliary and
verbose options used together.

Description

psr implements OLS with propensity score residual and IV
regression with instrument score residual. The first syntax fits OLS
and the second the IV regression. The covariates should be exogenous
always.

Examples

OLS with propensity score residuals

. use flu, clear

. psr outcome receive age female white copd heartd renal

. psr outcome receive age female white copd heartd renal, order(3)

. psr outcome receive age female white copd heartd renal, logit

. psr outcome receive age female white copd heartd renal, aux

. psr outcome receive age female white copd heartd renal, v

. psr outcome receive age female white copd heartd renal, vv

IV regression with instrument score residuals

. use nlsdat, clear

. gen d = ed76 > 12

. global X0 age76 black reg662-reg669 smsa66r

. global X1 $ smsa76r reg76r

. psr lwage76 (d = nearc4) $, vv

. psr lwage76 (d = nearc4) $, vv

Stored results

psr stores the following in e():

Scalars

e(N) number of observations

e(q) polynomial order for outcome prediction

Matrices

e(b) coefficient vector (average treatment effect)

e(V) variance of the estimator

e(b_bin) coefficient vector from probit/logit regression

e(V_bin) variance-covariance matrix of the estimators from probit/logit regression

e(b_aux) coefficient vector from auxiliary regression

e(b_aux) variance-covariance matrix from auxiliary regression

Macros

e(cmd) psr

e(depvar) name of output variable

e(model) ols or iv

e(pscmd) binary model classifier (probit or logit)

e(predictor) xb or pr

Functions

e(sample) marks estimation sample

Authors

Chirok Han

Department of Economics

Korea University

Seoul, Republic of Korea

chirokhan@korea.ac.kr

Myoung-jae Lee

Department of Economics

Korea University

Seoul, Republic of Korea

myoungjae@korea.ac.kr

References

Lee, M. J. 2018.
Simple least squares estimator for treatment effects using propensity
score residuals. Biometrika 105: 149-164.

Lee, M. J. 2021.
Instrument residual estimator for any response variable with endogenous
binary treatment.
Journal of the Royal Statistical Society (Series B) 83(3): 612-635.