Identifying Dynamic Sequential Plans

Abstract

We address the problem of identifying dynamic sequential plans in the framework of causal Bayesian networks, and show that the problem is reduced to identifying causal effects, for which there are complete identi cation algorithms available in the literature.

Appears in Proceedings of the Twenty-Fourth Conference on Uncertainty in Artificial Intelligence (UAI2008)

References in corpus (2)

• Probabilistic Evaluation of Sequential Plans from Causal Models with Hidden Variables
• Direct and Indirect Effects of Sequential Treatments

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• General Identification of Dynamic Treatment Regimes Under Interference
• Counterfactual Reasoning with Disjunctive Knowledge in a Linear Structural Equation Model
• Nonparametric causal effects based on incremental propensity score interventions
• The Proximal ID Algorithm