Ivtreatreg: A Command for Fitting Binary Treatment Models with Heterogeneous Response to Treatment and Unobservable Selection

In this article, I present ivtreatreg, a command for fitting four different binary treatment models with and without heterogeneous average treatment effects under selection-on-unobservables (that is, treatment endogeneity). Depending on the model specified by the user, ivtreatreg provides consistent estimation of average treatment effects by using instrumental-variables estimators and a generalized two-step Heckman selection model. The added value of this new command is that it allows for generalization of the regression approach typically used in standard program evaluation by assuming heterogeneous response to treatment. It also serves as a sort of toolbox for conducting joint comparisons of different treatment methods, thus readily permitting checks on the robustness of results.

[1]  Michael E. Sobel,et al.  What Do Randomized Studies of Housing Mobility Demonstrate? , 2006 .

[2]  P. Rosenbaum Interference Between Units in Randomized Experiments , 2007 .

[3]  G. Imbens,et al.  Implementing Matching Estimators for Average Treatment Effects in Stata , 2004 .

[4]  B. Sianesi,et al.  PSMATCH2: Stata module to perform full Mahalanobis and propensity score matching, common support graphing, and covariate imbalance testing , 2003 .

[5]  Giovanni Cerulli,et al.  Treatrew: A User-Written Command for Estimating Average Treatment Effects by Reweighting on the Propensity Score , 2014 .

[6]  D. Rubin Estimating causal effects of treatments in randomized and nonrandomized studies. , 1974 .

[7]  Matias D. Cattaneo,et al.  Efficient semiparametric estimation of multi-valued treatment effects under ignorability , 2010 .

[8]  J. Angrist,et al.  Instrumental Variables Estimation of Average Treatment Effects in Econometrics and Epidemiology , 1991 .

[9]  Thomas T. Semon,et al.  Planning of Experiments , 1959 .

[10]  Donald B. Rubin,et al.  Bayesian Inference for Causal Effects: The Role of Randomization , 1978 .

[11]  D. Katz The American Statistical Association , 2000 .

[12]  M. Hudgens,et al.  Toward Causal Inference With Interference , 2008, Journal of the American Statistical Association.

[13]  A. Nichols RD: Stata module for regression discontinuity estimation , 2016 .

[14]  Sascha O. Becker,et al.  Estimation of Average Treatment Effects Based on Propensity Scores , 2002 .

[15]  J. M. Villa DIFF: Stata module to perform Differences in Differences estimation , 2009 .

[16]  David M. Drukker,et al.  Estimation of Multivalued Treatment Effects under Conditional Independence , 2013 .

[17]  J. Heckman,et al.  The Economics and Econometrics of Active Labor Market Programs , 1999 .

[18]  C. Glymour,et al.  STATISTICS AND CAUSAL INFERENCE , 1985 .

[19]  J. Heckman Dummy Endogenous Variables in a Simultaneous Equation System , 1977 .