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PIRSA:23040118

Bounding counterfactual distributions in discrete structural causal models

APA

Tian, J. (2023). Bounding counterfactual distributions in discrete structural causal models. Perimeter Institute for Theoretical Physics. https://pirsa.org/23040118

Jin Tian Iowa State University

Talk numberPIRSA:23040118
Talk Type Conference
Subject

Abstract

We investigate the problem of bounding counterfactual queries from an arbitrary collection of observational and experimental distributions and qualitative knowledge about the underlying data-generating model represented in the form of a causal diagram. We show that all counterfactual distributions in an arbitrary structural causal model (SCM) with finite discrete endogenous variables could be generated by a family of SCMs with the same causal diagram where unobserved (exogenous) variables are discrete with a finite domain. Utilizing this family of SCMs, we translate the problem of bounding counterfactuals into that of polynomial programming whose solution provides optimal bounds for the counterfactual query.