Effects attributable to treatment: Inference in experiments and observational studies with a discrete pivot

By Paul R. Rosenbaum. 2001

86 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/78b47b8cefeddfe90517f294964de2ec4d56cd70

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Citations in the corpus

Machine Learning and Causal Inference : A Modular Approach to Assessing the Effects of the London Bombings of 2005 (2015, background): “To simplify the problem Rosenbaum (2001, 2002c) proposes a function of the τi as an object of scientific interest when outcomes are binary, writing A = ∑n i=1 Ziτi as the effect attributable to the treatment on the treated, or the “attributable effect.””
Attributing Effects to a Cluster-Randomized Get-Out-the-Vote Campaign (2009, methodology): “This is a 95% CI for A, the attributable effect (Rosenbaum 2001).”
Covariance Adjustment in Randomized Experiments and Observational Studies (2002, methodology): “Implementation in SAS has been discussed by Bergstralh, Kosanke and Jacobsen (1996) and Ming and Rosenbaum (2001).”
Inference on a New Class of Sample Average Treatment Effects (2017, methodology): “An example of when SATT can be the estimand of interest is an attributable treatment effect model in which the treatment effect varies across units (Rosenbaum 2001; Feng et al. 2014; Keele, Small, and Grieve 2017).”
Interference Between Units in Randomized Experiments (2007, methodology): “In randomized experiments without interference, the null randomization distribution may be inverted to obtain exact randomization inferences about the number of such exceedances caused by effects of the treatment (Rosenbaum 2001).”
Randomization Tests for Weak Null Hypotheses in Randomized Experiments (2018, background): “Although sometimes we can still wield the same FRTs, we need to modify the interpretations when the null hypothesis is not sharp (Rosenbaum 1999, 2001, 2003; Caughey, Dafoe, and Miratrix 2017).”