arXiv Machine Learning
Oct 8

Causal Posterior Estimation

The paper introduces Causal Posterior Estimation (CPE), a new Bayesian inference technique for simulator models where the likelihood is intractable but simulation is easy. CPE uses flow matching to approximate the posterior while embedding the model’s conditional dependencies directly into the neural network architecture. Experiments show that this hard‑coded structure yields highly accurate inference, matching or surpassing current state‑of‑the‑art methods.

By Simon Dirmeier, Antonietta Mira
Hugging Face Trending Papers
Jul 22

Local Causal Structure Learning in the Presence of Latent Variables and Selection Bias

Discovering the direct causes and effects of a target variable from observational data is a fundamental problem in causal discovery, with broad applications in domains such as gene regulatory analysis and biomedical research. Existing causal discovery methods either learn a global causal structure, which incurs substantial computational cost, or assume the absence of latent variables and selection bias, assumptions that are often violated in real-world settings.

Hugging Face Trending Papers
Jun 25

A Causal Foundation Model for Structure and Outcome Prediction

We introduce TabPFN-CFM, a causal foundation model that can handle multiple causal problems. TabPFN-CFM predicts both causal structure and outcomes from observational data, supports queries on all three levels of Pearl's Causal Hierarchy and uses known graph structure when available to improve predictions.