Causal inference using Stata: Estimating average treatment effects

4 days 3–4 hours daily

Learn how to use Stata’s treatment-effects estimators to estimate the effect caused by getting one treatment instead of another in observational data. We will discuss how observational data differ from experimental data and use the potential-outcomes framework to obtain the average treatment effect and the average treatment effect on the treated using a variety of estimators, including those suitable for endogenously assigned treatment. We will cover the conceptual and theoretical underpinnings of treatment effects as well as many examples using Stata.

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Course leader

Alvaro Fuentes Higuera

Senior Econometrician

Alvaro Fuentes Higuera is a Senior Econometrician at StataCorp LLC. His PhD research at the Leibniz Institute for Science and Mathematics Education in Kiel, Germany, focused on propensity-score methods for causal inference with multilevel data. At Stata, he produces documentation and other written materials and develops and presents trainings.

Prerequisite

A general familiarity with Stata and a graduate-level course in regression analysis or comparable experience.

Course topics

Treatment-effects estimators

Regression-adjustment estimator
Inverse-probability-weighting (IPW) estimator
Augmented IPW estimator
IPW regression-adjustment estimator
Nearest-neighbor matching estimator
Propensity-score matching estimator
Treatment-effects estimation using lasso
Difference-in-differences estimation
Causal forests and treatment-effect heterogeneity

Endogenous treatment effects within the potential-outcome framework

The double-robustness property of the augmented IPW and IPW regression-adjustment estimators

Using different functional forms for the outcome model and treatment model

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