An introduction to flexible methods for policy evaluation

Abstract

This chapter covers different approaches to policy evaluation for assessing the causal effect of a treatment or intervention on an outcome of interest. As an introduction to causal inference, the discussion starts with the experimental evaluation of a randomized treatment. It then reviews evaluation methods based on selection on observables (assuming a quasi-random treatment given observed covariates), instrumental variables (inducing a quasi-random shift in the treatment), difference-in-differences and changes-in-changes (exploiting changes in outcomes over time), as well as regression discontinuities and kinks (using changes in the treatment assignment at some threshold of a running variable). The chapter discusses methods particularly suited for data with many observations for a flexible (i.e. semior nonparametric) modeling of treatment effects, and/or many (i.e. high dimensional) observed covariates by applying machine learning to select and control for covariates in a data-driven way. This is not only useful for tackling confounding by controlling for instance for factors jointly affecting the treatment and the outcome, but also for learning effect heterogeneities across subgroups defined upon observable covariates and optimally targeting those groups for which the treatment is most effective.

Do soda taxes affect the consumption and health of school-aged children? Evidence from France and Hungary

“…Equation 5.5 describes the identification of the semi-parametric ATET based on inverse probability weighting (Huber, 2019). The outcome variable Y is multiplied by an inverse probability weight, where Π gives the share of treated observations in the post-treatment period and ρ d,t (X) is the probability of being in the treatment state d and in the time period t, conditional on covariates X.…”