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Causal inference using Stata: Estimating average treatment effects


Learn how and when to use Stata’s treatment-effects estimators to analyze treatment effects in observational data. Use regression adjustment, inverse probability weights, doubly robust methods, propensity-score matching, and covariate matching to estimate average treatment effects (ATEs) and ATEs on the treated. We will cover the conceptual and theoretical underpinnings of treatment effects as well as many examples using Stata.

After presenting the potential-outcome framework and discussing the parameters estimated, the course discusses a variety of estimators including

  • regression-adjustment estimator
  • inverse-probability-weighted (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
  • treatment-effects estimation for survival models

The course also discusses

  • double-robustness property of the augmented IPW and IPW regression-adjustment estimators
  • using different functional forms for outcome model and treatment model
  • multivalued treatments
  • estimators when the treatment is endogenous

The discussion of estimators that handle an endogenously assigned treatment includes extended regression model (ERM) estimators.

All topics are discussed using a combination of math and Stata examples.

Price: $1,395  

We offer a 15% discount for group enrollments of three or more participants.

Course leader

Joerg Luedicke portrait

Joerg Luedicke is a Senior Social Scientist and Statistician at StataCorp LLC. He has a master's degree in sociology from Free University Berlin, Germany, and a PhD in sociology from Bielefeld University, Germany. Prior to joining StataCorp LLC, he held positions as a research scientist and statistician at Free University Berlin, Bielefeld University, and Yale University. His interests lie in nonparametric statistics, survival analysis, discrete choice models, and causal inference.

Course topics

  • The potential-outcome framework, the average treatment effect, and the average treatment effect on the treated
  • Observational data differ from experimental data
  • Estimators:
    • Regression-adjustment estimator
    • Inverse-probability-weighted (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
    • Treatment-effects estimation for survival models
  • 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 outcome model and treatment model
  • Multivalued treatments
  • Extended regression models that allow the combination of endogenous treatment with endogenous sample selection and endogenous covariates


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

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Enrollment is limited. This course is offered in both classroom and web-based settings.

Classroom training courses are two-day courses that run from 8:30 a.m. to 4:30 p.m. each day. These courses take place at a training center where computers with Stata installed are provided. A continental breakfast, lunch, and an afternoon snack will also be provided; the breakfast is available before the course begins.

Web-based training courses are four-day courses that run for three and a half hours each day. You will be provided with a temporary Stata license to install on your computer, a printed copy of the course notes, and all the course datasets so that you can easily follow along. Learn more about how our web-based training courses work, watch a video demonstration, and find technical requirements for participating in this type of training.





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