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A Distinguished Speaker Series Seminar by Gary King, Ph.D.

We will cover how to detect and ameliorate model dependence, where small, indefensible changes in model specification have large, perhaps unintended impacts on our conclusions. Easy-to-use matching methods for both observational and experimental data will be discussed.

In observational data, matching methods can greatly reduce the problem of model dependence and improve the credibility of statistical conclusions.  In experimental data (i.e., when randomization is possible), these methods can drastically reduce financial costs, statistical uncertainty, and the time necessary to complete a study.

Along the way, we will show that propensity score matching, an enormously popular approach, accomplishes the opposite of its intended goal — increasing imbalance, inefficiency, model dependence, and bias — and should be replaced with other matching methods in applications. Fortunately, other matching methods, such as coarsened exact matching, are far easier to use and understand and more powerful statistically.

Easy-to-use software is available for all methods introduced.