Matched Sampling for Causal Effects: The Use of Matched Sampling and Regression Adjustment 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…”
Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference (2007, methodology): “1 Our approach is similar in spirit to, or a generalization of the ideas in, Cochran and Rubin (1973), Rubin (1973, 1979), Rosenbaum and Rubin (1984), Rubin and Thomas (2000), and Imai and van Dyk (2004) who each recommend matching followed by a different form of parametric adjustment, as well as…”
Some Practical Guidance for the Implementation of Propensity Score Matching (2008, methodology): “Rubin (1973, 1979) first proposed several matched sample regression adjustments in the context of Mahalanobis metric matching and they have been more recently discussed by Abadie and Imbens (2006a) and Imbens (2004).”
The Social Impact of Social Funds in Jamaica: A ‘Participatory Econometric’ Analysis of Targeting, Collective Action, and Participation in Community-Driven Development (2005, background): “The idea behind the propensity score [Rubin, 1973; Rosenbaum and Rubin ,1983] is to create an index that summarises the exogenous observable attributes of an individual.”
The Augmented Synthetic Control Method (2018, methodology): “This is analogous to bias correction for inexact matching (Rubin, 1973; Abadie and Imbens, 2011).”
Unemployed and their caseworkers: should they be friends or foes? (2010, unknown): “As a third modification of the conventional one-to-one matching estimator, we also reduce bias by a weighted regression procedure that was recommended by Imbens (2004) and related to proposals by Rubin (1973) and Robins and Rotnitzky (1995).”
Bayesian Nonparametric Modeling for Causal Inference (2011, methodology): “Many causal methods for observational data (Rubin 1973, 1979), including many recent methods (for instance, Heckman, Ichimura, and Todd 1997; Robins and Ritov 1997; Robins, Hernan, and Brumback 2000; Rubin and Thomas 2000; Hirano, Imbens, and Ridder 2003; Kurth et al. 2006), involve fitting both a…”
On the Validity of the Regression Discontinuity Design for Estimating Electoral Effects: New Evidence from over 40,000 Close Races (2014, unknown): “Note: See Appendix A in the supporting information for details on each data set.”
Implementing Matching Estimators for Average Treatment Effects in Stata (2004, methodology): “Following Rubin (1973) and Abadie and Imbens (2002), we approximate these regression functions by linear functions and estimate them using least squares on the matched observations.”
A confounding bridge approach for double negative control inference on causal effects (2018, unknown): “The impact of observed confounders on causal inference can be alleviated by direct adjustment methods such as inverse probability weighting, matching, regression, and doubly robust methods (Bang & Robins, 2005; Rosenbaum & Rubin, 1983b; Rubin, 1973; Stuart, 2010).”
A Distributional Approach for Causal Inference Using Propensity Scores (2006, background): “Values of d between 1/8 and 1/2 and of σ 21 /σ 2 0 between 1/2 and 2 are typical of those that might occur in practice (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).”