Dagma-DCE: Interpretable, Non-Parametric Differentiable Causal Discovery

Autor:	Waxman, Daniel, Butler, Kurt, Djuric, Petar M.
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Statistics - Methodology Statistics - Machine Learning
Zdroj:	IEEE Open Journal of Signal Processing, vol. 5, pp. 393-401, 2024
Druh dokumentu:	Working Paper
DOI:	10.1109/OJSP.2024.3351593
Popis:	We introduce Dagma-DCE, an interpretable and model-agnostic scheme for differentiable causal discovery. Current non- or over-parametric methods in differentiable causal discovery use opaque proxies of ``independence'' to justify the inclusion or exclusion of a causal relationship. We show theoretically and empirically that these proxies may be arbitrarily different than the actual causal strength. Juxtaposed to existing differentiable causal discovery algorithms, \textsc{Dagma-DCE} uses an interpretable measure of causal strength to define weighted adjacency matrices. In a number of simulated datasets, we show our method achieves state-of-the-art level performance. We additionally show that \textsc{Dagma-DCE} allows for principled thresholding and sparsity penalties by domain-experts. The code for our method is available open-source at https://github.com/DanWaxman/DAGMA-DCE, and can easily be adapted to arbitrary differentiable models. Comment: 9 pages, 2 figures. Accepted to the IEEE Open Journal of Signal Processing
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2401.02930 Zobrazit plný text záznamu View this record from Arxiv