arXiv AI June 2, 2026 By Usef Faghihi

Topological Ignorability for Structural Causal Effects Beyond Means

arXiv:2606. 01184v1 Announce Type: cross Abstract: Many interventions alter the structure of an outcome distribution rather than its mean: they can split a population into disconnected regimes, create loops or holes, generate branches, or reorganize an outcome cloud while leaving the average response nearly unchanged.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

benchmarks

arXiv AI

Jun 6

Beyond Means: Topological Causal Effects under Persistent-Homology Ignorability

arXiv:2603. 14169v2 Announce Type: replace-cross Abstract: Average treatment effects (ATE) and conditional average treatment effects (CATE) are foundational causal estimands, but they target changes in expected outcomes and can miss treatment-induced changes in the shape of outcome distributions.

By Amir Saki, Usef Faghihi

arXiv AI

Jun 2

Causal Density Functions

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.

By Sridhar Mahadevan

benchmarks

arXiv Machine Learning

Sep 10

Representation Learning for Sample-Efficient CATE Estimation by Leveraging Multiple Outcomes

arXiv:2609.06294v1 Announce Type: new Abstract: Estimating conditional average treatment effects (CATE) enables efficient targeting of interventions, but many applications have limited experimental s...

By Maitreyi Swaroop, Shikha Bhat, Samantha Rodriguez, Tamar Krishnamurti, Bryan Wilder

safety

arXiv AI

Jul 17

Generative Synthetic Data for Causal Inference: Pitfalls, Remedies, and Opportunities

arXiv:2604. 23904v3 Announce Type: replace-cross Abstract: Synthetic tabular data are often evaluated by distributional similarity, privacy distance, or train-on-synthetic-test-on-real predictive performance, but these criteria do not ensure validity for causal inference.

By Yichen Xu

llms diffusion benchmarks safety

arXiv Machine Learning

Sep 25

GeoDose-CP: Graph-Local Conformal Inference for Continuous-Treatment Earth Observation

GeoDose-CP introduces a graph‑local conformal inference framework for estimating localized stochastic potential outcomes when dealing with continuous or mixed continuous‑atomic treatments in Earth observation data. The method jointly models intervention‑induced treatment shifts, outcome‑scale Jacobians, and spatial residual dependence, and includes exact weighted candidate inversion, a scalable sparse approximation, and a refusal mechanism for inadequate support. Evaluation on controlled experiments, MineDoseBench, and a multi‑mine study in New South Wales demonstrates high selective coverage and identifies limitations when longitudinal treatment data are unavailable.

By Md Khalid Hasan Sakib, Dristi Datta, Manoranjan Paul, Davina White

arXiv AI

Jun 19

Latent Confounded Causal Discovery via Lie Bracket Geometry

arXiv:2606. 19610v1 Announce Type: cross Abstract: Recent work on Kan-Do-Calculus (KDC) has established that the boundary between passive observation and active intervention in causal inference is a category-theoretic bi-adjunction, with interventions modeled by left Kan extensions and conditioning by right Kan extensions.

By Sridhar Mahadevan

diffusion

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Related stories

Beyond Means: Topological Causal Effects under Persistent-Homology Ignorability

Causal Density Functions

Representation Learning for Sample-Efficient CATE Estimation by Leveraging Multiple Outcomes

Generative Synthetic Data for Causal Inference: Pitfalls, Remedies, and Opportunities

GeoDose-CP: Graph-Local Conformal Inference for Continuous-Treatment Earth Observation

Latent Confounded Causal Discovery via Lie Bracket Geometry