CEM: Software for Coarsened Exact Matching
Stefano Iacus, Gary King, Giuseppe Porro. 2009.
"CEM: Software for Coarsened Exact Matching".
Journal of Statistical Software, 30(9).
Abstract
This program is designed to improve causal inference via a method of matching that is widely applicable in observational data and easy to understand and use (if you understand how to draw a histogram, you will understand this method). The program implements the coarsened exact matching (CEM) algorithm, described below. CEM may be used alone or in combination with any existing matching method. This algorithm, and its statistical properties, are described in Iacus, King, and Porro (2008).
See Also
- [Paper] A Theory of Statistical Inference for Matching Methods in Causal Research (2019)
- [Paper] Causal Inference Without Balance Checking: Coarsened Exact Matching (2012)
- [Paper] CEM: Coarsened Exact Matching in Stata (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)
- [Paper] Multivariate Matching Methods That Are Monotonic Imbalance Bounding (2011)
- [Paper] The Balance-Sample Size Frontier in Matching Methods for Causal Inference (2017)