Description
This book constitutes the revised selected papers of the 6th Pacific Causal Inference Conference, PCIC 2024, held in Shanghai, China, during July 5–6, 2024.
The 8 papers included in these proceedings were carefully reviewed and selected from 15 submissions. They aim to promote research and developmental activities in the fields of Causal Inference and Artificial Intelligence.
Table of Contents
Avoiding the Unconfoundednes Assumption: Counterfactual Inference Considering Unobserved Confounders.-Causal Inference in the Multiverse of Hazard.- A Continuous Structural Intervention Distance to Compare Causal Graphs.- Detection Windows from Hidden Markov Model for Discovering Varying Causal Relations Between Time Series.- Endogenous Confounding in Causal Decomposition Analysis.- Evaluation Criteria for Causal Discovery Without Ground-truth Graphs.- Optimizing Experimental Design for Causal Effect Estimation with Partial Measurements.- Exploring the Use of Q-learning in Causal Inference for Adaptive Interventions.
