Covariance Adjustment in Randomized Experiments and Observational Studies

By Paul R. Rosenbaum. 2002

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Citations in the corpus

Causal Inference through a Witness Protection Program (2014, methodology): “Such covariate sets are often found in real-world problems, and form the basis of many of the observational studies done in practice (Rosenbaum, 2002a).”
The Econometrics of Randomized Experiments (2016, methodology): “See for additional discussions Lesaffre and Senn (2003), Samii and Aronow (2012), Rosenbaum (2002), Lin (2013), Schochet (2010), Young (2016), Senn (1994).”
Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program (2010, background): “Rosenbaum (2002a) discusses the use of outcomes “known to be unaffected by the treatment” to evaluate the presence of hidden biases.”
The Effectiveness of Distance Education across Virginia's Community Colleges: Evidence from Introductory College-Level Math and English Courses (2011, methodology): “A sensitivity analysis following Rosenbaum' s (2002) method showed that the results for course attrition would be sensi-”
Monotone Comparative Statics for Models of Politics (2006, background): “…limited claims nonparametrically, without making any assumptions except those necessary for monotonicity.17 This can be done, for example, by using rank tests, either on the raw data or on the residuals from regressions of the variables of interest on a vector of control variables (Rosenbaum 2002).”
A general approach to causal mediation analysis. (2010, unknown): “The degree of sensitivity can be calibrated either in comparison to other studies (Rosenbaum, 2002, p. 325) or in conjunction with expert opinion (e.g., White, Carpenter, Evans, & Schroter, 2007).”
The Generalized Oaxaca-Blinder Estimator (2020, methodology): “Rosenbaum (2002) suggested forming a confidence interval by inverting a Fisher randomization test based on the residuals of an arbitrary (possibly nonlinear) model; this satisfies 1, 3, and (arguably) 4, but the validity of the confidence interval requires a constant additive treatment effect.”
What You Can and Can’t Properly Do with Regression (2010, background): “Unfortunately, the elephant in the room remains: there sensitivity analyses can suggest that the omitted variables may not be important (Rosenbaum 2002a, Chap.”
High-dimensional regression adjustments in randomized experiments (2016, background): “…the number of samples grows to inﬁnity, it is now well-established that regression adjustments are always asymptotically helpful—even in misspeciﬁed models—provided we add full treatment-by-covariate interactions to the regression design and use robust standard errors [2, 3, 4, 5, 6, 7, 8, 9, 10].”