[gov3009-l] King on "Matching for Causal Inference Without Balance Checking"

Justin Grimmer jgrimmer at fas.harvard.edu
Mon Sep 29 20:47:36 EDT 2008


Dear Applied Statistics Community,



Please join us on Wednesday October 1st when Gary King, the David Florence
Professor of Government, will present "Matching for Causal Inference Without
Balance Checking".  A draft of the paper is available here:
http://gking.harvard.edu/files/cem.pdf , and here is the abstract:



We address a major discrepancy in matching methods for causal inference in
observational data.  Since these data are typically plentiful, the goal of
matching is to reduce bias and only secondarily to keep variance low.
However, most matching methods seem designed for the opposite problem,
guaranteeing sample size ex ante but limiting bias by controlling for
covariates through reductions in the imbalance between treated and control
groups only ex post and only sometimes.  (The resulting practical difficulty
may explain why many published applications do not check whether

imbalance was reduced and so may not even be decreasing bias.) We introduce
a new class of "Monotonic Imbalance Bounding" (MIB) matching methods that
enables one to choose a fixed level of maximum imbalance, or to reduce
maximum imbalance for one variable without changing it for the others. We
then discuss a specific MIB method called "Coarsened Exact Matching" (CEM)
which, unlike most existing approaches, also explicitly bounds through ex
ante user choice both the degree of model dependence and the causal effect
estimation error, eliminates the need for a separate procedure to restrict
data to common support, meets the congruence principle, is approximately
invariant to measurement error, works well with modern methods of imputation
for missing data, is computationally efficient even with massive data sets,
and is easy to understand and use. This method can improve causal inferences
in a wide range of applications, and may be preferred for simplicity of use
even when it is possible to design superior methods for particular problems.
We also make available open source software which implements all our
suggestions.





The applied statistics workshop meets in room K-354, CGIS–Knafel (1737
Cambridge St) at 12 noon, with a light-lunch served.  The presentation will
begin at 1215 and the workshop usually ends around 130.  All are welcome to
attend,



Cheers
Justin Grimmer
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