Simplifying Matching Methods for Causal Inference (University of Wisconsin at Madison)

Presentation Date: 

Monday, October 11, 2021


University of Wisconsin at Madison, Department of Population Health Sciences

Presentation Slides: 

We show how to use matching methods for causal inference to ameliorate model dependence -- where small, indefensible changes in model specification have large impacts on our conclusions. We introduce methods that are simpler, more powerful, and easier to understand than existing approaches. We also show that propensity score matching, an enormously popular approach, often accomplishes the opposite of its intended goal -- increasing imbalance, inefficiency, model dependence, and bias -- and should be replaced with other matching methods in applications.  See for papers and easy-to-use software to implement all methods discussed.