Amortized Bayesian Causal Discovery of Extended Factor Graphs
Learning causal graphs from interventional data is a challenging problem with broad applications. In molecular biology, for example, a central goal is to uncover gene regulatory networks from large-scale perturbation data. An ideal algorithm for this task should scale to thousands of nodes, incorporate interventions even when their targets are unknown, quantify uncertainty, and provide identifiability guarantees. However, existing approaches---e.g. approaches using score-based optimization or approximate Bayesian inference---often fail to meet all of these criteria. To address these limitations, we develop Amortized Bayesian Causal Discovery of Extended Factor Graphs (ABCDEFG). Our method...
| Authors | Yichen Gu, Yuxuan Song, Weizhou Qian et al. |
|---|---|
| Primary category | stat.ML |
| Published | 24 July 2026 |
| Last updated at source | 24 July 2026 |
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