Causal learning for partially observed stochastic dynamical systems

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


Many models of dynamical systems have causal interpretations that support reasoning about the consequences of interventions, suitably defined. Furthermore, local independence has been suggested as a useful independence concept for stochastic dynamical systems. There is, however, no well-developed theoretical framework for causal learning based on this notion of independence. We study independence models induced by directed graphs (DGs) and provide abstract graphoid properties that guarantee that an independence model has the global Markov property w.r.t. a DG. We apply these results to Itô diffusions and event processes. For a partially observed system, directed mixed graphs (DMGs) represent the marginalized local independence model, and we develop, under a faithfulness assumption, a sound and complete learning algorithm of the directed mixed equivalence graph (DMEG) as a summary of all Markov equivalent DMGs.

Original languageEnglish
Title of host publication34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018
EditorsAmir Globerson, Amir Globerson, Ricardo Silva
Number of pages11
PublisherAssociation For Uncertainty in Artificial Intelligence (AUAI)
Publication date2018
ISBN (Electronic)9781510871601
Publication statusPublished - 2018
Event34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018 - Monterey, United States
Duration: 6 Aug 201810 Aug 2018


Conference34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018
LandUnited States
SponsorBerg Health, Disney Research, et al., Alphabet Inc., Microsoft Research, Uber

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