Econometric techniques for estimating treatment effects

Zhehui Luo ()

North American Stata Users' Group Meetings 2004 from Stata Users Group

Abstract: One way to evaluate the econometric techniques of estimating treatment effects is to use experimental data to gauge results of different methods (LaLonde 1986 AER). There has been heated debate since LaLonde!'s seminal paper as to whether the propensity score techniques overcome the selection problem (Smith & Todd 2003, Dehejia 1999, 2002). This study uses a randomized trial of cognitive behavioral intervention on reducing the severity of symptoms and their impact on emotional distress and physical function for cancer patients. We use several other datasets from which cancer patients were selected as comparison groups. We estimate the "true" treatment effect on physical function and mental health (SF-36) with the randomized trial and compare the results of the following econometric techniques using the comparison groups: (1) difference-in-differences (DID) method, (2) instrumental variables, and (3) propensity score matching estimators (including nearest neighbor, radius matching, stratification and kernel matching) (Becker & Ichino, 2002). The results show that the propensity score matching depends on the comparison samples and the outcome compared and the bias is larger when the sample is more different from the treated group.

Date: 2004-07-15
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