mlsanityData to 14 Oct 2026

Causal Posterior Estimation

arXiv 2505.21468 ↗First posted 27 May 2025cs.LG stat.MLcited by 1

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Abstract (arXiv)

We present Causal Posterior Estimation (CPE), a novel method for Bayesian inference in simulator models, where evaluating the likelihood function is intractable or computationally expensive, but generating outputs given parameter values is straightforward. CPE approximates the posterior distribution using flow matching while directly incorporating the conditional dependence structure induced by the model's graphical representation into the neural network architecture. Across extensive experiments, we demonstrate that hard-coding these conditional dependencies into the network, rather than requiring them to be learned from data, enables CPE to achieve highly accurate posterior inference that matches or outperforms state-of-the-art baselines.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 74 of its 119 references that are in the map. First posted on arXiv 27 May 2025.

Builds on

74 of 119 references.

References · 74

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38 more
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50 most cited of 74 All 74 (JSON)

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Citing papers per year: 2025: 1.

1 paper.

  1. 2025Diffusion Models in Simulation-Based Inference: A Tutorial Reviewcited by 11