Statistics and Data Science Seminar, Statistics Department, UT Austin

Date: 10/16/2020


Difference-in-differences (DD) is a commonly used approach in policy evaluation for identifying the impact of an intervention or treatment. Under a parallel trend assumption, we can recover a causal effect by comparing the difference in outcomes between a treatment and a control group, both before and after an intervention was set in place. However, time-varying confounders often break the identifying assumption, biasing our estimates. In this paper, I identify the different contexts in which matching can help reduce such biases, and show how balancing covariates directly can yield better results for solving these issues and bound causal estimates. I also show how this method can be applied both for panel and repeated cross-sectional data. I illustrate these results with simulations and a case study of the impact of a new voucher scheme on socioeconomic segregation in Chile

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