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Causal Inference Through the Structural Causal Marginal Problem
Luigi Gresele · Julius von Kügelgen · Jonas Kübler · Elke Kirschbaum · Bernhard Schölkopf · Dominik Janzing

Tue Jul 19 02:25 PM -- 02:30 PM (PDT) @ None

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.

Author Information

Luigi Gresele (MPI for Intelligent Systems, Tübingen)
Julius von Kügelgen (MPI for Intelligent Systems, Tübingen & University of Cambridge)
Jonas Kübler (Max Planck Institute for Intelligent Systems, Tübingen)
Elke Kirschbaum (Amazon Web Services)
Bernhard Schölkopf (MPI for Intelligent Systems Tübingen, Germany)

Bernhard Scholkopf received degrees in mathematics (London) and physics (Tubingen), and a doctorate in computer science from the Technical University Berlin. He has researched at AT&T Bell Labs, at GMD FIRST, Berlin, at the Australian National University, Canberra, and at Microsoft Research Cambridge (UK). In 2001, he was appointed scientific member of the Max Planck Society and director at the MPI for Biological Cybernetics; in 2010 he founded the Max Planck Institute for Intelligent Systems. For further information, see www.kyb.tuebingen.mpg.de/~bs.

Dominik Janzing (Amazon)

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