Related papers: Bayesian Intervention Optimization for Causal Discovery

Bayesian Intervention Optimization for Causal Discovery

URL: http://arxiv.org/abs/2406.10917v1
Date: Sun, 16 Jun 2024 12:45:44 GMT
Title: Bayesian Intervention Optimization for Causal Discovery
Authors: Yuxuan Wang, Mingzhou Liu, Xinwei Sun, Wei Wang, Yizhou Wang,
Abstract summary: Causal discovery is crucial for understanding complex systems and informing decisions. Current methods, such as Bayesian and graph-theoretical approaches, do not prioritize decision-making. We propose a novel Bayesian optimization-based method inspired by Bayes factors.
Score: 23.51328013481865
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Causal discovery is crucial for understanding complex systems and informing decisions. While observational data can uncover causal relationships under certain assumptions, it often falls short, making active interventions necessary. Current methods, such as Bayesian and graph-theoretical approaches, do not prioritize decision-making and often rely on ideal conditions or information gain, which is not directly related to hypothesis testing. We propose a novel Bayesian optimization-based method inspired by Bayes factors that aims to maximize the probability of obtaining decisive and correct evidence. Our approach uses observational data to estimate causal models under different hypotheses, evaluates potential interventions pre-experimentally, and iteratively updates priors to refine interventions. We demonstrate the effectiveness of our method through various experiments. Our contributions provide a robust framework for efficient causal discovery through active interventions, enhancing the practical application of theoretical advancements.

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