Yuya Sasaki

Brian and Charlotte Grove Chair 

& Professor of Economics

Department of Economics

& Data Science Institute

Vanderbilt University

Research Field: Econometrics


Frequently Asked Questions about the Stata Command  "robustate":

Q1. How does the  "robustate"  command compare with the existing IPW estimator such as the  "teffects ipw"  command?

Q2. How does the  "robustate"  command compare with the IPW estimation with trimming/truncating small propensity scores?

Q3. How does the  "robustate"  command compare with the matching estimators such as  "teffects pamatch"  and  "teffects nnmatch"  commands?

Q4. How does the  "robustate"  command compare with the overlap weighting approaches?

See further details about the Stata Command  "robustate".

October 2021: Cluster robust double machine learning package now available in R and Python - thanks to Malte S. Kurz.

Stata Command: 


Diagnostic testing of outliers. Use this command to check if your estimates and standard errors are credible in regress and ivregress.

See the testout page

Stata Command: 


Estimation of the average treatment effects (ATE) robustly against the limited overlap or a weak satisfaction of the common support condition.

See the robustate page