Matched Sampling for Causal Effects: Matching to Remove Bias in Observational Studies

By Donald B. Rubin. 1973

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Citations in the corpus

FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference (2017, background): “In the 1970’s and 1980’s, a large literature on different dimen-sion reduction approaches to matching was developed (e.g., Rubin, 1973a,b, 1976; Cochran and Rubin, 1973) with the extreme being work on propensity score matching, which was later extended to work on penalized regression approaches…”
Deep Counterfactual Networks with Propensity-Dropout (2017, methodology): “Throughout this paper, we adopt Rubin’s potential outcomes model (Rubin, 2011; 1973; Rosenbaum & Rubin, 1983).”
Combining Qualitative and Quantitative Methods to Evaluate Participation in Costa Rica's Program of Payments for Environmental Services (2009, methodology): “, in those studies in which the random assignment to treatment to units (subjects) is absent (Rubin, 1973).”
Green Innovation and Financial Performance (2013, background): “Matched pairs are designed to reduce bias and to increase precision in observational studies (Rubin, 1973).”
Interpretable Almost-Exact Matching for Causal Inference (2019, methodology): “Early on, this led to techniques that reduce dimension using propensity score matching (Rubin, 1973b,a, 1976; Cochran and Rubin, 1973), which extend to penalized regression approaches (Schneeweiss et al., 2009; Rassen and Schneeweiss, 2012; Belloni et al., 2014; Farrell, 2015).”
Minimal dispersion approximately balancing weights: asymptotic properties and practical considerations (2017, unknown): “Rubin, D. B. (1973).”
Local level climate justice? Adaptation finance and vulnerability reduction (2013, background): “Matching maximizes probabilistic equivalence (Rubin, 1973), and minimizes imbalance and thus bias between covariates (Blackwell”
Matching With Multiple Control Groups With Adjustment for Group Differences (2008, methodology): “…of background covariates, have been receiving increasing attention over the past few decades in fields such as statistics (e.g., Rosenbaum, 2002; Rubin, 1973a, 1973b), economics (e.g., The work for this article was done while Elizabeth Stuart was a graduate student at Harvard University…”
The Community College Route to the Bachelor’s Degree (2015, methodology): “Fortunately, the pioneers of the counterfactual method demonstrated that it is unnecessary to exactly match each treated subject with each untreated subject on all covariates (Heckman et al., 1998; Rosenbaum, 1989; Rosenbaum & Rubin, 1983b; Rubin, 1973).”
Large Sample Properties of Matching Estimators for Average Treatment Effects (2004, methodology): “For unit i, with i = 1 N, following Rubin (1973), let Y i (0) and Y i (1) denote the two potential outcomes given the control treatment and given the active treatment, respectively.”