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arXiv AI By Zheyu Zhang (University of Michigan), Jiayuan Dong (University of Michigan), Jie Liu (University of Michigan), Xun Huan (University of Michigan)

Goal-Oriented Sequential Bayesian Experimental Design for Causal Learning

Read the original on arXiv AI →

GO-CBED is a goal‑oriented Bayesian framework for sequential causal experimental design that directly maximizes expected information gain on user‑specified causal quantities, rather than inferring the full causal model. It employs a variational lower bound estimator optimized with a transformer‑based policy network and normalizing flow posteriors, enabling real‑time, non‑myopic decision making. Experiments on synthetic structural causal models and semi‑synthetic gene regulatory networks show that GO‑CBED consistently outperforms existing baselines, especially under limited experimental budgets and complex causal mechanisms.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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