AI RESEARCH
Causal Density Functions
arXiv CS.AI
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ArXi:2606.00754v1 Announce Type: cross \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. We derive practical estimators for do-curves and directed edge scores, relate the construction to Radon-Nikodym/Kan semantics for conditioning and intervention, and evaluate the resulting estimators on synthetic and real perturbation benchmarks.