Why Propensity Scores Should Not Be Used for Matching


Gary King and Richard Nielsen. Working Paper. “Why Propensity Scores Should Not Be Used for Matching”. Copy at http://j.mp/2oTKhnd
Paper386 KB
Supplementary Appendix539 KB
Why Propensity Scores Should Not Be Used for Matching


We show that propensity score matching (PSM), an enormously popular method of preprocessing data for causal inference, often accomplishes the opposite of its intended goal --- thus increasing imbalance, inefficiency, model dependence, and bias. The weakness of PSM comes from its attempts to approximate a completely randomized experiment, rather than, as with other matching methods, a more efficient fully blocked randomized experiment. PSM is thus uniquely blind to the often large portion of imbalance that can be eliminated by approximating full blocking with other matching methods. Moreover, in data balanced enough to approximate complete randomization, either to begin with or after pruning some observations, PSM approximates random matching which, we show, increases imbalance even relative to the original data. Although these results suggest researchers replace PSM with one of the other available matching methods, propensity scores have other productive uses.

Last updated on 10/18/2018