Should observational studies be designed to allow lack of balance in covariate distributions across treatment groups?

By Donald B. Rubin. 2009

119 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/0f80edfc9cbac2cca91049ccfae970bebfd11083

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In Statistics and Probability

Citations in the corpus

TRYGVE HAAVELMO AND THE EMERGENCE OF CAUSAL CALCULUS (2014, unknown): “This has led researchers to surmise that one should condition on all such covariates (Hirano and Imbens, 2001; Pearl, 2009b; Rubin, 2009).”
EIGHT MYTHS ABOUT CAUSALITY AND STRUCTURAL EQUATION MODELS (2013, background): “…this equivalence, potential outcome advocates have continued to view SEM as a danger to scientific thinking, labeling it an " unprincipled " " confused theoretical perspective, " " bad practical advice, " " theoretical infatuation, " and " nonscientific ad hockery " (Rubin 2009, Pearl 2009a).”
Overlap in observational studies with high-dimensional covariates (2017, background): “A key argument for high-dimensional observational studies is that unconfoundedness is more plausible when the analyst adjusts for more covariates (Rosenbaum, 2002; Rubin, 2009).”
Covariate balancing propensity score by tailored loss functions (2016, methodology): “Later in the seminal work of Rosenbaum and Rubin (1983), the propensity score, defined as the conditional probability of receiving treatment given the covariates, was established as a fundamental tool to adjust for imbalance in more than just a few covariates.”
Causal inference in statistics: An overview (2009, background): “Rubin (2009) goes as far as stating that refraining from conditioning on an available measurement is “nonscientific ad hockery” for it goes against the tenets of Bayesian philosophy (see (Pearl, 2009b,c; Heckman and Navarro-Lozano, 2004) for a discussion of this fallacy). would be deemed to have no…”
A new criterion for confounder selection (2011, background): “Following Rubin (2009), we believe it is generally implausible that an investigator will know the complete underlying causal structure for the data-generating process.”
The Logic of Causal Inference (2010, unknown): “; Rubin, 2009; Kraemer et al., 2008; Fleming and DeMets, 1996; Green et al., 2010).”
Do Analysts Understand the Economic and Reporting Complexities of Derivatives (2016, unknown): “14 Because matching models do not require exclusion restrictions, the general rule is to include a comprehensive list of covariates when estimating propensity scores (Rubin 2009).”