Biostatistics Weekly Seminar

A nonparametric projection-based estimator for the probability of causation

Maria Cuellar, PhD
University of Pennsylvania

Researchers often need to determine whether a specific exposure, or something else, caused an individual's outcome. To answer questions of causality in which the exposure and outcome have already been observed, researchers have suggested estimating the probability of causation (PC). PC is especially important in court, for example in class action lawsuits, and in public and health policy, for example in determining who has benefitted most from a program. However, the current estimation methods for PC make strong parametric assumptions, or are inefficient and do not easily yield inferential tools. In this talk, I will describe an influence-function-based nonparametric estimator for a projection of PC, which allows for simple interpretation and valid inference by making only weak structural assumptions. I compare my proposed estimator to the current plug-in methods, both parametric and nonparametric, by simulation. Finally, I present an application of the proposed estimator by using data from a randomized controlled trial in Western Kenya.

Zoom (Link to Follow)
24 February 2021

Speaker Itinerary

Topic revision: r2 - 13 Feb 2021, AndrewSpieker

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