Identifying Dynamic Sequential Plans

2012/06/13 by Jin Tian, Tian, Jin · 3 citations
Computer Science · Decision Sciences · #AI-based Problem Solving and Planning #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #Data Quality and Management #FOS: Computer and information sciences

paper · pdf · doi:10.48550/arxiv.1206.3292

openalex publication_date 2012/06/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Cited by

• General Identification of Dynamic Treatment Regimes Under Interference
• Causal Inference Under Unmeasured Confounding With Negative Controls: A Minimax Learning Approach
• The Proximal ID Algorithm

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