Causal learning for partially observed stochastic dynamical systems
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Standard
Causal learning for partially observed stochastic dynamical systems. / Mogensen, Søren Wengel; Malinsky, Daniel; Hansen, Niels Richard.
34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018. red. / Amir Globerson; Amir Globerson; Ricardo Silva. Bind 1 Association For Uncertainty in Artificial Intelligence (AUAI), 2018. s. 350-360.Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
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 -
ID: 212432948