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This paper establishes a precise mathematical correspondence between graph surgery and the do-operator in acyclic structural causal models, proving their equivalence in terms of dependency graphs.
This paper formalises counterfactual policy optimisation for Markov Decision Processes under probabilistic nondeterministic causal models, which separate latent confounding from inherent stochasticity, and proposes a practical optimisation procedure for deriving robust counterfactual policies. The approach is validated on a sepsis treatment simulator with diabetes as an unobserved global confounder.
This paper extends Pearl's structural causal model framework by introducing causal zeros and causal differential equations to handle symmetric constraints and feedback cycles, which are not allowed in directed acyclic graphs.
This paper presented at ICML explores how causal and statistical models can generalize to novel combinations of interacting objects, with a poster session scheduled at the conference.