YSPH Biostatistics Seminar: “Causal graphical approaches to handling missing-not-at-random mechanisms”

NOTE: BIS 525 students are required to attend in person. Others are invited to attend in person, but may also attend via Zoom.

SPEAKER: Razieh Nabi, PhD, Assistant Professor, Department of Biostatistics and Bioinformatics, Rollins School of Public Health at Emory University

TITLE: “Causal graphical approaches to handling missing-not-at-random mechanisms”

ABSTRACT: It is often said that the fundamental problem of causal inference (CI) is a missing data problem -- the comparison of responses to two hypothetical treatment assignments is made difficult because for every experimental unit only one potential response is observed. In this talk, we consider the implications of the converse view: that missing data problems are a form of CI. We make explicit how the missing data problem of recovering the complete data law from the observed data law can be viewed as identification of a joint distribution over counterfactual variables corresponding to values had we (possibly contrary to fact) been able to observe them. Drawing analogies with CI, we show how identification assumptions in missing data can be encoded in terms of graphical models defined over counterfactual and observed variables. We note interesting similarities and differences between missing data and CI theories. The validity of any identification or estimation result relies on the assumptions encoded by the graph holding true. Thus, we also provide new insights on the testable implications of a few common classes of missing data models, and design goodness-of-fit tests for them.

YSPH values inclusion and access for all participants. If you have questions about accessibility or would like to request an accommodation, please contact Charmila Fernandes at charmila.fernandes@yale.edu. We will try to provide accommodations requested by October 19, 2023.