Genetic Matching for Estimating Causal Effects: A General Multivariate Matching Method for Achieving Balance in Observational Studies

By A. Diamond, Jasjeet S. Sekhon. 2006

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Citations in the corpus

FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference (2017, methodology): “, 2015), (2) 1-PSNNM with oracle variable selection, (3) Genetic Matching (GenMatch) (Diamond and Sekhon, 2013), (4) Causal Forest (Wager and Athey, 2018), (5) Mahalanobis Matching, (6) double linear regression, (7) BART (Chipman et al.”
Newspaper Closures Polarize Voting Behavior (2018, methodology): “We use genetic matching (Diamond & Sekhon, 2013) to construct optimal weights for matching, which leverages a genetic algorithm (Mebane & Sekhon, 2011) that maximizes balance between treated (or “closure,” as we will refer to them) and control (or “comparison”) units, while discarding as little…”
Entropy Balancing is Doubly Robust (2015, background): “Recently, there is an emerging interest, particularly among applied researchers, in directly incorporating covariate balance in the estimation procedure, so there is no need to check covariate balance repeatedly (e.g. Diamond and Sekhon, 2013; Imai and Ratkovic, 2014; Zubizarreta, 2015).”
Referral to an extracorporeal membrane oxygenation center and mortality among patients with severe 2009 influenza A(H1N1). (2011, methodology): “Compared with matching on propensity score alone, GenMatch matching has been shown to reduce covariate imbalance and bias from confounding.(19,20) Previous cohort studies have assessed the relative effectiveness of other clinical interventions using GenMatch matching to balance baseline covariates.”
Propensity score matching and variations on the balancing test (2011, neutral): “7 An interesting suggestion by Diamond and Sekhon (2008) is to use genetic matching, which is an approach for performing multivariate matching that uses an evolutionary search algorithm to determine the weight given to each covariate. Their approach avoids the problem of multiple acceptable specific”
Generalized Optimal Matching Methods for Causal Inference (2016, methodology): “Since the introduction of optimal matching, a variety of other methods for matching on covariates have been developed, including coarsened exact matching [5], genetic matching [6], combining optimal matching with nearfine balance on one stratification [7], and using integer programming to 1 In the c”
Randomization Inference in the Regression Discontinuity Design: An Application to Party Advantages in the U.S. Senate (2015, background): “Political scientists have been aware of the methodological difficulties since at least the 1970s (see Erikson, 1971), and Gelman and King (1990) clarified many of the inferential challenges to interpreting this advantage as a causal effect by defining the incumbency advantage estimand in terms of co”