Probabilistic Counterfactual Inference for Discrete Outcomes in Gaussian-Process Causal Models

Paper recorded by Signals 4 on 2026-10-06 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-06 on arXiv · recorded by Signals 4 on 2026-10-07

Category: cs.LG · 机器学习 · first seen 2026-10-07

Abstract

Counterfactual inference in Gaussian-process structural causal models (GP-SCMs) has been developed primarily for continuous endogenous variables, limiting applicability to causal graphs that contain discrete child nodes with continuous parents. We introduce a unified probabilistic framework for counterfactual inference with heterogeneous variable types by pairing GP predictors with explicit exogen

Read on arXiv →