Causal Inference Without Balance Checking: Coarsened Exact Matching
Abstract
We discuss a method for improving causal inferences called “Coarsened Exact Matching’’ (CEM), and the new “Monotonic Imbalance Bounding’’ (MIB) class of matching methods from which CEM is derived. We summarize what is known about CEM and MIB, derive and illustrate several new desirable statistical properties of CEM, and then propose a variety of useful extensions. We show that CEM possesses a wide range of desirable statistical properties not available in most other matching methods, but is at the same time exceptionally easy to comprehend and use. We focus on the connection between theoretical properties and practical applications. We also make available easy-to-use open source software for R and Stata which implement all our suggestions.
See also An Explanation of CEM Weights.
See Also
- [Presentation] Matching for Causal Inference Without Balance Checking (2009)
- [Dataset] Replication data for: Causal Inference Without Balance Checking: Coarsened Exact Matching
- [Paper] A Theory of Statistical Inference for Matching Methods in Causal Research (2019)
- [Paper] CEM: Coarsened Exact Matching in Stata (2009)
- [Paper] CEM: Software for Coarsened Exact Matching (2009)
- [Paper] Comparative Effectiveness of Matching Methods for Causal Inference (2011)
- [Paper] Matching As Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference (2007)
- [Paper] MatchIt: Nonparametric Preprocessing for Parametric Causal Inference (2011)