Causal Density Functions 文章

ArXiv CS.AI2026-06-02NEWSen作者: Sridhar Mahadevan

摘要

arXiv:2606.00754v1 Announce Type: cross Abstract: We introduce causal density functions: Radon-Nikodym derivatives that compare interventional laws to observational laws and therefore act as local density ratios for causal effects. Whereas many causal-strength measures compare whole distributions after graph surgery, causal density functions provide a pointwise change-of-measure object that can be estimated, calibrated, and used to score directed influence. The basic identity \[ \mathbb{E}_{\mathrm{do}}[f(Y)] = \mathbb{E}_{\mathrm{obs}}\!\left[f(Y)\rho(X,Y)\right] \] makes causal density directly testable: if the estimated density ratio is correct, observational expectations reweighted by $\rho$ reproduce interventional expectations.

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Causal Density Functions
2026-06-02PRODUCT_LAUNCH影响: MEDIUM

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