Why Propensity Scores Should Not Be Used for Matching
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e6c25d79572fe9a8fae7b32c201d8a04d529.pdf
Description
Submitted version
Size
395.1 KB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
King, Gary
Nielsen, Richard Alexander
Date Issued
May 2019
Journal
Political Analysis
Publisher
Cambridge University Press (CUP)
Citation
King, Gary and Richard Nielsen. "Why Propensity Scores Should Not Be Used for Matching." Political Analysis 27, 4 (May 2019): 435-454. © 2019 The Author(s)
Version
Original manuscript
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Political Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1017/pan.2019.11
