Title:      Matching for Causal Inference Without Balance
Checking

Authors:    Stefano Iacus, Gary King, Giuseppe Porro

Entrydate:  2008-06-26 18:53:53

Keywords:   Matching, causal inference, observational data,
missing data, 

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.


http://polmeth.wustl.edu/retrieve.php?id=760

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