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 Distinguished Speaker Series Seminar by Stephen Raudenbush, Ed.D.

Over the past several decades, a paradigm shift in statistics has transformed causal inference methods in social science and medicine. The key idea is simple: each study participant possesses a potential outcome under each possible intervention. Causal effects are then comparisons of these potential outcomes.

This way of defining causal effects has generated a host of new concepts and methods in randomized experiments, natural experiments, and observational studies. Our aim in this workshop is to apply these concepts and methods in multilevel settings. In such settings, causal effects are generated by the actions of heterogeneous agents (e.g., teachers, social workers, police, and physicians) operating in varied organizational settings (e.g., schools, clinics, precincts, hospitals). The fundamental challenge, but also a key focus of interest, is the heterogeneity of causal effects that arise in multilevel settings.

• The first session of the seminar will focus on randomized trials and how we might define and estimate the average and variance of “intent-to-treat” effects, “complier average causal effects,” and mediation effects, with a focus on multi-site trials and trials in which whole clusters (e.g., classrooms, schools, or hospitals) are the unit of random assignment. We’ll challenge conventional thinking on fixed versus random effects models and propose alternatives.
• The second session will extend these methods for multilevel observational studies using analysis of covariance, matching, and weighting.
• The third session will consider natural experiments in multilevel settings using regression discontinuity (RDD) and difference-in-difference (DID) methods.

In each session, we’ll consider modeling decisions, software choices, and assumption checks. Illustrative data sets include the National Head Start Impact Study, the Tennessee Class Size experiment, the Early Childhood Longitudinal Study, and High School Math reform in Chicago, with references to other similar studies.

Prerequisites: Knowledge of multiple regression. A basic introduction to methods of causal inference is helpful but not required.

Relevant Reference

Raudenbush, S.W., Schwartz, D., (2020). Randomized experiments in education, with implications for multilevel causal inference. Annual Review of Statistics and Its Application. 7:1, 177-208.