Omitted Labels in Causality: A Study of Paradoxes

Abstract

We explore “omitted label contexts,” in which training data is limited to a subset of the possible labels. This setting is standard among specialized human experts or specific focused studies. By studying Simpson’s paradox, we observe that ``correct’’ adjustments sometimes require non-exchangeable treatment and control groups. A generalization of Simpson’s paradox leads us to study networks of conclusions drawn from different contexts, within which a paradox of nontransitivity arises. We prove that the space of possible nontransitive structures in these networks exactly corresponds to structures that form from aggregating ranked-choice votes.

Publication
To appear in the 4th Conference on Causal Learning and Reasoning (CLeaR)
Bijan Mazaheri
Bijan Mazaheri

My interests include mixture models, high level data fusion, and stability to distribution shift - usually through the lense of causality.