In this article, we revise the estimation of the dose-response function described in Hirano and Imbens (2004, Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives, 73-84) by proposing a flexible way to estimate the generalized propensity score when the treatment variable is not necessarily normally distributed. We also provide a set of programs that accomplish this task. To do this, in the existing doseresponse program (Bia and Mattei, 2008, Stata Journal 8: 354-373), we substitute the maximum likelihood estimator in the first step of the computation with the more flexible generalized linear model.
In this article, we describe tvdiff, a community-contributed command that implements a generalization of the difference-in-differences estimator to the case of binary time-varying treatment with pre- and postintervention periods. tvdiff is flexible and can accommodate many actual situations, enabling the user to specify the number of pre- and postintervention periods and a graphical representation of the estimated coefficients. In addition, tvdiff provides two distinct tests for the necessary condition of the identification of causal effects, namely, two tests for the so-called parallel-trend assumption. tvdiff is intended to simplify applied works on program evaluation and causal inference when longitudinal data are available.
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