Causal Inference without Balance Checking: Coarsened Exact Matching

By Stefano Maria Iacus, Gary King, Giuseppe Porro. 2012

3,362 citations recorded by the source corpus.

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Citations in the corpus

FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference (2017, methodology): “CEM is best suited for numerical data and the R package does not automatically handle more than 15 covariates.”
Recruiting for Ideas: How Firms Exploit the Prior Inventions of New Hires (2010, positive): “To achieve more stringent matching, we employ the CEM approach (Iacus et al. 2009).”
Improving Survey Response Rates in Online Panels (2016, methodology): “Nevertheless, as a validation check, we test the robustness of our results using a nonparametric matching procedure known as Coarsened Exact Matching (CEM; Iacus, King, & Porro, 2012).”
Can You Gig It? An Empirical Examination of the Gig Economy and Entrepreneurial Activity (2018, methodology): “To execute this next text we pre-process our data, applying a Coarsened Exact Match (CEM) (Blackwell et al. 2009, Iacus et al. 2009, 2012), based on three covariates: population in the local area (to account for market size), average weekly wage (to account for the differences in average local…”
The Rise of the Sharing Economy: Estimating the Impact of Airbnb on the Hotel Industry (2017, positive): “While various matching methods exist, here we use the Coars-ened Exact Matching (CEM) procedure (Iacus et al., 2012), because it is intuitive and works well with categorical data (like most hotel characteristics).”
Ebalance: A Stata Package for Entropy Balancing (2013, neutral): “Future work may consider how entropy balancing could be combined with other matching methods that are implemented in Stata such as nnmatch (Abadie, Drukker, Herr, and Imbens 2004), psmatch2 (Leuven and Sianesi 2003), or cem (Iacus, King, and Porro 2009).”
A Survey on Causal Inference (2020, methodology): “ches is the lower variance, because we use more information to create counterfactual outcome. Here, we also want to introduce another matching method called Coarsened Exact Matching (CEM) proposed in [56]. Because either the 1-k matching or the full matching fails to consider the extrapolation regio”
Enhancing Rigor in Quantitative Entrepreneurship Research (2019, background): “However, PSM has been found to suffer from important limitations (King & Nielsen, 2019), with coarsened exact matching (CEM) being recommended as a robust alternative (Iacus et al., 2009; 2012; King & Nielsen, 2019).”
Creating College Opportunity: School Counselors and Their Influence on Postsecondary Enrollment (2013, methodology): “…(MIB) and which offer users a clear and decidedly effective course through which to reduce the statistical bias associated with their estimates (Iacus et al. 2012).7 Finally, since CEM operates within sample space and in the data space where X is generated, users can make robust inferences…”
Green bonds as a tool against climate change? (2021, positive): “Formally, we perform the matching using a coarsened exact matching methodology (Iacus et al., 2012).”
Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates (2020, background): “Coarsened exact matching (CEM) matches on discretized raw values of the observed confounders (Iacus et al., 2012).”
Interpretable Almost-Exact Matching for Causal Inference (2019, methodology): “For example, Coarsened Exact Matching (Iacus et al., 2012, 2011) is almost-exact if one were to use an oracle (should one ever become available) that bins covariates according to importance for estimating causal effects.”