Causal learning for partially observed stochastic dynamical systems

TY - GEN

T1 - Causal learning for partially observed stochastic dynamical systems

AU - Mogensen, Søren Wengel

AU - Malinsky, Daniel

AU - Hansen, Niels Richard

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85059388834&partnerID=8YFLogxK

M3 - Article in proceedings

AN - SCOPUS:85059388834

VL - 1

SP - 350

EP - 360

BT - 34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018

A2 - Globerson, Amir

A2 - Globerson, Amir

A2 - Silva, Ricardo

PB - Association For Uncertainty in Artificial Intelligence (AUAI)

T2 - 34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018

Y2 - 6 August 2018 through 10 August 2018

ER -