mlsanityData to 7 Oct 2026

Causal Density Functions

arXiv 2606.00754 ↗First posted 30 May 2026stat.ME cs.AI cs.LGcited by 2

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Abstract (arXiv)

We study the full density ratio between a specified intervention regime Pa and an observational regime P0, ρa=dPa/dP0, under the prerequisite Pall P0. We call the regime-indexed ratio a causal density function when Pa is an identified or directly observed interventional law. The underlying Radon-Nikodym derivative and the identity mathbbEa[f(Z)] = mathbbE0!left[f(Z)ρa(Z)right] are classical importance weighting, not new identification results. Our narrower question is whether retaining the entire pointwise ratio is useful as a reusable diagnostic across several downstream functionals. We evaluate a two-density plug-in baseline through held-out moment transport and overlap stress tests on synthetic and perturbation data. We also report a pairwise graph-scoring heuristic as a negative result: its F1 is (0.10) on a synthetic DAG, (0.12) on Sachs, and (0.33) on a multi-regime chain. These experiments do not establish an estimation advantage over direct density-ratio, inverse-probability, Riesz, or doubly robust methods; they instead delimit what the pointwise ratio target and the present plug-in estimator do and do not provide.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 6 of its 19 references that are in the map. First posted on arXiv 30 May 2026.

Builds on

6 of 19 references.

References · 6

  1. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 745
  2. 2019Gradient-Based Neural DAG Learningcited by 227
  3. 2020Differentiable Causal Discovery from Interventional Datacited by 218
  4. 2017Permutation-based Causal Inference Algorithms with Interventionscited by 78
  5. 2019A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statisticscited by 55
  6. 2025Intuitionistic j-Do-Calculus in Topos Causal Modelscited by 4

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Citing papers per year: 2026: 2.

2 papers.