Event Phone: 1-610-715-0115

Cancellation Policy: If you cancel your registration at least two weeks before the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50).

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 4-Day Livestream Seminar Taught by Noah Greifer, Ph.D

This course offers an in-depth introduction to matching and weighting methods using R. Researchers use matching and weighting to estimate the causal effect of a treatment on an outcome — such as the effect of smoking on health or the effect of divorce on child outcomes — when assignment to the treatment is not random. Many of these techniques rely on traditional propensity scores, but the course will also cover newer techniques that do not. The R packages MatchIt and WeightIt (authored by the instructor) allow users to implement all of these techniques using a unified and easy-to-use syntax.

Matching and weighting are powerful, flexible methods that allow for the incorporation of substantive knowledge while providing transparency about the trade-offs that are often masked by other methods of estimating causal effects. Their outputs can be assessed and interpreted easily both by analysts and audiences, making them especially effective for medical and policy research.

Though the course is focused on these methods, other key scientific and methodological issues will be discussed, including communicating results, data visualization, and managing trade-offs between theoretical performance and interpretability.

This seminar is both conceptual and practical. We will briefly introduce the potential outcomes framework to motivate matching and weighting methods for causal inference. We will also discuss the conceptual differences between types of effects, including average treatment effects (ATEs) and average treatment effects on the treated (ATTs).

The course will then guide you through foundational methods like propensity score matching and inverse probability weighting to more modern methods that use machine learning and optimization (though prior knowledge of these topics is not required). We will discuss methods for evaluating the performance of matching and weighting methods, and end with estimating treatment effects, performing inference, and writing up the results of an analysis. You will get practical experience by working through exercises from the social and health sciences