Causal Inference Through the Structural Causal Marginal Problem

• URL: http://arxiv.org/abs/2202.01300v2
• Date: Fri, 4 Feb 2022 13:48:55 GMT
• Title: Causal Inference Through the Structural Causal Marginal Problem
• Authors: Luigi Gresele, Julius von K\"ugelgen, Jonas M. K\"ubler, Elke Kirschbaum, Bernhard Sch\"olkopf, Dominik Janzing
• Abstract summary: We introduce an approach to counterfactual inference based on merging information from multiple datasets. We formalise this approach for categorical SCMs using the response function formulation and show that it reduces the space of allowed marginal and joint SCMs.
• Score: 17.91174054672512
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce an approach to counterfactual inference based on merging information from multiple datasets. We consider a causal reformulation of the statistical marginal problem: given a collection of marginal structural causal models (SCMs) over distinct but overlapping sets of variables, determine the set of joint SCMs that are counterfactually consistent with the marginal ones. We formalise this approach for categorical SCMs using the response function formulation and show that it reduces the space of allowed marginal and joint SCMs. Our work thus highlights a new mode of falsifiability through additional variables, in contrast to the statistical one via additional data.

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