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Matching for Causal Inference Without Balance Checking
Stefano Iacus, Department of Economics, Business and Statistics, University of Milan, IT
Gary King, Institute for Quantitative Social Science, Harvard University
Giuseppe Porro, Department of Economics and Statistics, University of Trieste
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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.
SUGGESTED CITATION:
Stefano Iacus, Gary King, and Giuseppe Porro,
"Matching for Causal Inference Without Balance Checking"
(June 2008).
UNIMI - Research Papers in Economics, Business, and Statistics.
Statistics and Mathematics.
Working Paper 36.
http://services.bepress.com/unimi/statistics/art36
Paper presented by C. Tommasi.
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