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Activity Number: 460 - Causal Methods for Discovery, Confirmation and Mechanistic Evaluation
Type: Contributed
Date/Time: Thursday, August 6, 2020 : 10:00 AM to 2:00 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #309911
Title: Gaussian Prepivoting for Finite Population Causal Inference
Author(s): Peter Cohen* and Colin Fogarty
Companies: Massachusetts Institute of Technology and Massachusetts Institute of Technology
Keywords: Prepivoting; Regression adjustment; Rerandomization; Weak null; Randomization test

In finite population causal inference, exact randomization tests can be constructed for sharp null hypotheses. Oftentimes inference is instead desired for the weak null that the sample average of the treatment effects takes on a particular value while leaving the subject-specific treatment effects unspecified. Without proper care, tests valid for sharp null hypotheses may be anti-conservative should only the weak null hold, creating the risk of misinterpretation when randomization tests are deployed in practice. We develop a general framework for unifying modes of inference for sharp and weak nulls, wherein a single procedure simultaneously delivers exact inference for sharp nulls and asymptotically valid inference for weak nulls. To do this, we employ randomization tests based upon prepivoted test statistics, wherein a test statistic is first transformed by a suitably constructed cumulative distribution function and its randomization distribution assuming the sharp null is then enumerated. The versatility of the method is demonstrated through a host of examples, including rerandomized designs and regression-adjusted estimators in completely randomized designs.

Authors who are presenting talks have a * after their name.

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