Causal Inference

May 22-26, 2023
10:00 am – 3:30 pm (Eastern US time)
Replay Access: 1 year from end of workshop
Instructor: Doug Steinley
Software Demonstrations: Conducted live in R

Student: $795
Professional: $995

Category: Livestream, Workshops

Causal Inference is a five-day workshop focused on the application and interpretation of quasi-experimental and graphical models in an attempt to reveal causal structures. The workshop explores the basics of causal inference, including potential outcomes, counterfactuals, confounding, and mediation. The traditional “gold standard” of randomization is discussed as a motivating factor as we evaluate methods for revealing causation in quasi-experimental settings, such as: differences-in-differences, propensity scores, and regression discontinuity. In addition to traditional statistical methodology, we also will focus on graphical approaches such as mediation, directed acyclic graphs, and structural causal models. Participants will learn which approach is likely to be most useful for revealing causal information for their research question


Doug Steinley, Ph.D.

Doug Steinley is a Professor in the Department of Psychology at the University of Missouri. His research and teaching focus on multivariate statistical methodology, with a primary interest in cluster analysis (both traditional procedures and more modern mixture modeling techniques) and social network analysis. Read More

Workshop Details


In an effort to continually improve our instruction we obtain student evaluations with each course offering. Here is a sample of reviews from our prior online offerings of Causal Inference:

Doug is a clear, effective, and extremely knowledgeable presenter (I don't know how he stayed so sharp after those long days, especially ending with Pearl!).

Extremely important and timely topic matter. The conceptual introduction was helpful, and I really liked the idea of providing a foundation in ANOVA and Regression, which presented an effective framework for thinking about the quasi-experimental methods covered.

The software demonstrations are really helpful! Going over things in theory is useful, but seeing the code and results really helps cement the info

I really enjoyed the course. I learned a lot and appreciated the opportunity to think about causal inference deeply

Overall, great workshop.

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