Hugging Face Trending Papers
A Semi-Analytical Inference Framework for Structural Causal Models
Read the original on Hugging Face Trending Papers →The Flow has not summarised this story yet — read it at Hugging Face Trending Papers.
Related stories
arXiv AI
4d ago
A Semi-Analytical Inference Framework for Structural Causal Models
The paper introduces a semi‑analytical inference framework for Structural Causal Models (SCMs) that performs message passing directly on the SCM using a common finite‑dimensional function space. It represents structural mechanisms and probability densities with tensor‑product B‑spline bases, enabling forward propagation via a numerical Law of the Unconscious Statistician and backward conditioning through spline projection. Experiments on randomly generated continuous SCMs show that the method accurately approximates Monte Carlo solutions and improves with higher basis resolution.
arXiv Machine Learning
6d ago
DeDiCa: A Scalable Deconfounding Discrete Causal Generative Model
DeDiCa is a scalable framework for causal inference with discrete variables and hidden confounding. It uses a tensorized parameterization of causal Bayesian networks, allowing efficient maximum‑likelihood training on observational data while exactly marginalizing over hidden confounders. The model can estimate any (proxy‑)identifiable interventional query with CPU‑efficient training and millisecond‑scale inference, matching state‑of‑the‑art neural generative models in ATE estimation while requiring less memory and time.
arXiv Machine Learning
Jun 17
Instrumental and Proximal Causal Inference with Gaussian Processes
arXiv:2603. 02159v2 Announce Type: replace-cross Abstract: Instrumental variable (IV) and proximal causal learning (Proxy) methods are central frameworks for causal inference in the presence of unobserved confounding.
arXiv Machine Learning
Oct 2
CIDER-FM: Foundation Models for Causal Inference from Diverse Experimental Regimes
CIDER-FM is a causal foundation model that combines finite observational data with surrogate-interventional datasets to predict target conditional interventional distributions more accurately than using observational data alone. It employs an intervention-aware representation and hierarchical three‑axis attention to integrate information across variables, samples, and experimental regimes. Experiments on synthetic graphs, simulated data, and real‑world Causal Chambers data show that incorporating experimental context improves CID prediction performance.
arXiv Machine Learning
Jul 17
Causal Inference for Sequential Settings under Interference and Latent Confounding
arXiv:2607. 14940v1 Announce Type: new Abstract: We study causal inference under outcome interference for sequential, observational settings.
arXiv Machine Learning
Jun 19
Unsupervised Causal Abstractions Discovery
arXiv:2606. 19594v1 Announce Type: new Abstract: Causal abstractions formalize when a high-level structural causal model (SCM) captures the interventional behavior of a lower-level SCM.