Matched Sampling for Causal Effects: Using Multivariate Matched Sampling and Regression Adjustment to Control Bias in Observational Studies

By Donald B. Rubin. 2006

623 citations recorded by the source corpus.

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Citations in the corpus

Evaluating the Differential Effects of Alternative Welfare-to-Work Training Components : A Re-Analysis of the California GAIN Program * by (2005, background): “32 See Smith and Todd (2005) for a critical re-analysis of Dehejia and Wabba (1999).”
When to Control for Covariates? Panel Asymptotics for Estimates of Treatment Effects (2004, background): “1 See, for example, Cochran (1965), Rubin (1973, 1979), or Rosenbaum (1995, Chapter 9) for discussions of caliper and nearest-neighbor matching, or Heckman et al. (1997) for examples of nonparametric smoothing.”
Evaluation Methods for Non‐Experimental Data (2005, background): “However, this does not ensure 6Rosenbaum and Rubin, 1985; Rubin, 1979. that the same happens within any sample, and it is, in fact, a strong assumption when programmes are directed to tightly specified groups.”
The performance of estimators based on the propensity score (2013, methodology): “For example, Rubin (1979) suggested combining PM with (parametric) regression adjustment to take into account the fact that treated and controls with exactly the same propensity score are usually very rare or non-existent.25 This idea has been taken up again by Abadie and Imbens (2006) who show 25…”
Challenges With Propensity Score Strategies in a High-Dimensional Setting and a Potential Alternative (2011, background): “Additional covariance adjustment is often preferred, however, in case balance across groups is insufficient or simply to achieve efficiency gains (Rubin, 1979; Rubin & Thomas, 2000).”
Sensitivity of Matching-Based Program Evaluations to the Availability of Control Variables (2013, methodology): “This property implies that the estima- tor remains consistent if either the matching step is based on a correctly specified selection model, or the regression model is correctly specified (e.g. Rubin, 1979; Joffe, Ten Have, Feldman, and Kimmel, 2004).”
Matching With Multiple Control Groups With Adjustment for Group Differences (2008, methodology): “We refer to the conditional expectations in Equations 2 and 3 as response surfaces, using the terminology common in experimental design and used in both Cochran and Rubin (1973) and Rubin (1979), among others.”
Using Propensity Scores to Help Design Observational Studies: Application to the Tobacco Litigation (2001, methodology): “…after obtaining measured outcomes, models relating outcomes to observed covariates are very likely to reach improved (reduced bias, more precise) inferences, in analogy with covariance adjustments in randomized block experiments (Rubin, 1970, 1973b, 1979; Roseman, 1998; and Rubin and Thomas, 2000).”
Matching on Balanced Nonlinear Representations for Treatment Effects Estimation (2017, methodology): “They work well when there are a few covariates with normal distributions [34].”
The Validity and Precision of the Comparative Interrupted Time-Series Design (2016, methodology): “To account for any small imbalances, the RCT treatment estimates referenced below come from fully adjusted models that include all pretest and demographic covariates (Rubin, 2008). For further details on the analysis of the RCTs, see St.Clair et al. (2014) and Konstantopoulos, Miller, and van der Pl”
Matching for Balance, Pairing for Heterogeneity in an Observational Study of the Effectiveness of For-Profit and Not-For-Profit High Schools in Chile (2014, methodology): “harleson Indexin health services research [Deyo, Cherkin and Ciol (1992)]. Obviously, one can match for both such a summary score and a few key covariates using some form of the Mahalanobis distance. Rubin (1979) found that covariance adjustment of matched pair diﬀerences is a particularly robust te”