Showing our opus magna to other Stata users
Since the seminal paper of Rosenbaum and Rubin, propensity score (PS) has played a significant role in the causal inference framework. It aims to indicate similar units that will be matched or to provide appropriate weights. PS has gained its great popularity by dramatically reducing dimensionality in estimation. Further development of related methods has turned the attention of researchers to the dual nature of PS as a covariate balancing score and conditional probability of treatment assignment. Imai and Ratkovic (2014) exploit the aforementioned duality by deriving a set of appropriate moment conditions and thereby introduce a PS estimator that optimizes the covariate balance—covariate balancing propensity score (CBPS). The paper introduces a new Stata user-written function CBPS that implements the CBPS method within a generalized method of moments framework. The short description of the estimator and the function is presented. Additionally, an empirical exercise that concerns a relationship between a woman's fertility and her labor supply using the exogenous variation due to twin births (Rosenzweig and Wolpin 1980; Angrist and Evans 1998) is provided. The paper also compares the CBPS method with classical PS estimators in unfavorable data environment of a high degree of heterogeneity among women, low fraction of twin births, and exogeneity of the treatment variable with respect to covariates. Moreover, to my knowledge, this is the first paper that concerns the labor supply of Polish women using the first-birth twins identification strategy.