After preprocessing in this way, any method of analysis that would have been used without matching can be applied to estimate causal effects, although some methods will have even better properties. Matching is a nonparametric method of preprocessing data to control for some or all of the potentially confounding influence of pretreatment control variables by reducing imbalance between the treated and control groups. " Causal Inference Without Balance Checking: Coarsened Exact Matching" (Political Analysis, 2012) and " Multivariate Matching Methods That are Monotonic Imbalance Bounding" (JASA, 2011), “ CEM: Coarsened Exact Matching in Stata” (Stata Journal, 2009, with Matthew Blackwell), “ CEM: Software for Coarsened Exact Matching.” (Journal of Statistical Software, 2009), “ A Theory of Statistical Inference for Matching Methods in Causal Research” (2017). The program implements the Coarsened Exact Matching (CEM) algorithm described in: This program is designed to improve the estimation of causal effects via an extremely powerful method of matching that is widely applicable and exceptionally easy to understand and use (if you understand how to draw a histogram, you will understand this method). Authors: Stefano Iacus, Gary King, Giuseppe Porro
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