[2509.12981] Causal Discovery via Quantile Partial Effect

Computer Science > Machine Learning arXiv:2509.12981 (cs) [Submitted on 16 Sep 2025 (v1), last revised 4 Apr 2026 (this version, v2)] Title:Causal Discovery via Quantile Partial Effect Authors:Yikang Chen, Xingzhe Sun, Dehui Du View a PDF of the paper titled Causal Discovery via Quantile Partial Effect, by Yikang Chen and 2 other authors View PDF HTML (experimental) Abstract:Quantile Partial Effect (QPE) is a statistic associated with conditional quantile regression, measuring the effect of covariates at different levels. Our theory demonstrates that when the QPE of cause on effect is assumed to lie in a finite linear span, cause and effect are identifiable from their observational distribution. This generalizes previous identifiability results based on Functional Causal Models (FCMs) with additive, heteroscedastic noise, etc. Meanwhile, since QPE resides entirely at the observational level, this parametric assumption does not require considering mechanisms, noise, or even the Markov assumption, but rather directly utilizes the asymmetry of shape characteristics in the observational distribution. By performing basis function tests on the estimated QPE, causal directions can be distinguished, which is empirically shown to be effective in experiments on a large number of bivariate causal discovery datasets. For multivariate causal discovery, leveraging the close connection between QPE and score functions, we find that Fisher Information is sufficient as a statistical measure...