Goal-Oriented Sequential Bayesian Experimental Design for Causal Learning

• URL: http://arxiv.org/abs/2507.07359v1
• Date: Thu, 10 Jul 2025 00:53:57 GMT
• Title: Goal-Oriented Sequential Bayesian Experimental Design for Causal Learning
• Authors: Zheyu Zhang, Jiayuan Dong, Jie Liu, Xun Huan,
• Abstract summary: GO-CBED is a goal-oriented Bayesian framework for sequential causal experimental design.<n>The framework is both non-myopic, optimizing over entire intervention sequences, and goal-oriented, targeting only model aspects relevant to the causal query.<n>We demonstrate that GO-CBED consistently outperforms existing baselines across various causal reasoning and discovery tasks.
• Score: 2.89781371591051
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present GO-CBED, a goal-oriented Bayesian framework for sequential causal experimental design. Unlike conventional approaches that select interventions aimed at inferring the full causal model, GO-CBED directly maximizes the expected information gain (EIG) on user-specified causal quantities of interest, enabling more targeted and efficient experimentation. The framework is both non-myopic, optimizing over entire intervention sequences, and goal-oriented, targeting only model aspects relevant to the causal query. To address the intractability of exact EIG computation, we introduce a variational lower bound estimator, optimized jointly through a transformer-based policy network and normalizing flow-based variational posteriors. The resulting policy enables real-time decision-making via an amortized network. We demonstrate that GO-CBED consistently outperforms existing baselines across various causal reasoning and discovery tasks-including synthetic structural causal models and semi-synthetic gene regulatory networks-particularly in settings with limited experimental budgets and complex causal mechanisms. Our results highlight the benefits of aligning experimental design objectives with specific research goals and of forward-looking sequential planning.

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