Albert J. Weatherhead III University Professor
Director, Institute of Quantitative Social Sciences
Harvard University
Abstract
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.
