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In the unlikely event that Statistical Horizons LLC must cancel a seminar, we will do our best to inform you as soon as possible of the cancellation. You would then have the option of receiving a full refund of the seminar fee or a credit towards another seminar. In no event shall Statistical Horizons LLC be liable for any incidental or consequential damages that you may incur because of the cancellation.

A 3-Day Livestream Seminar Taught by Gonzalo Vazquez-Bare, Ph.D.

This course covers the statistical foundations and practical aspects of difference-in-differences (DiD) models. DiD models are one of the most popular tools for causal inference and policy evaluation in non-experimental settings. The main idea behind them is to compare the evolution over time of the outcome of a group of units (such as individuals, households, counties, firms, etc.) that are exposed to some intervention with the evolution of the outcomes of a group of units that are unaffected by the intervention. Under certain assumptions, DiD models allow the researcher to learn about the causal effect of the intervention by flexibly controlling for unobserved heterogeneity and common time trends. The use of DiD models is widespread in economics, political science, education, sociology, health sciences, environmental sciences, and many other areas.

After a brief review of panel data methods, we will introduce the potential outcomes framework to rigorously define causal effects and to study classical results and recent advances in identification, estimation, and statistical inference for DiD models.

After taking this workshop, you will be able to:

• • Understand the mechanics and potential pitfalls of linear regression methods for estimating treatment effects with panel / longitudinal data.
  • Apply novel DiD methods to estimate treatment effects when these effects can vary both across units and over time.
  • Provide empirical support for the validity of the identification assumptions behind DiD models and conduct sensitivity analysis to assess the robustness of the estimation results.