[gov3009-l] Applied Statistics - 9/16 - Gary King

Aaron Kaufman aaronkaufman at fas.harvard.edu
Mon Sep 14 09:48:50 EDT 2015


Hi everyone!

This week at the Applied Statistics Workshop we will be welcoming *Gary
King*, the Albert J. Weatherhead III University Professor at Harvard
University.  He will be presenting work entitled *Why Propensity Scores
Should Not Be Used for Matching*.  Please find the abstract below and on
the website
<http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/presentations/gary-king-harvard-title-coming-soon>
.

As usual, we will meet in CGIS Knafel Room 354 from noon to 1:30pm, and
lunch will be provided.  See you all there!

-- Aaron Kaufman

Title: Why Propensity Scores Should Not Be Used for Matching
Abstract: Researchers use propensity score matching (PSM) as a data
preprocessing step to selectively prune units prior to applying a model to
estimate a causal effect. The goal of PSM is to reduce imbalance in the
chosen pre-treatment covariates between the treated and control groups,
thereby reducing the degree of model dependence and potential for bias. We
show here that PSM often accomplishes the opposite of what is intended --
increasing imbalance, inefficiency, model dependence, and bias. The
weakness of PSM is that it attempts to approximate a completely randomized
experiment, rather than, as with other matching methods, a more powerful
fully blocked randomized experiment. PSM, unlike other matching methods, is
thus blind to the often large portion of imbalance that could have been
eliminated by approximating full blocking. Moreover, in data balanced
enough to approximate complete randomization, either to begin with or after
pruning some observations, PSM approximates random matching which turns out
to increase imbalance. For other matching methods, the point where
additional pruning increases imbalance occurs much later in the pruning
process, when full blocking is approximated and there is no reason to
prune, and so the danger is considerably less. We show that these problems
with PSM occur even in data designed for PSM, with as few as two
covariates, and in many real applications. Although these results suggest
that researchers replace PSM with one of the other available methods when
performing matching, propensity scores have many other productive uses.

-- 
Aaron R Kaufman
PhD Candidate, Harvard University
Department of Government
818.263.5583
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