arXiv Machine Learning July 14, 2026 By Roberto Faleh, Sofia Morelli, Holger Brandt

Representation Learning for Semiparametric Causal Mediation Analysis under No Essential Heterogeneity

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arXiv:2607. 10540v1 Announce Type: cross Abstract: We propose a two-stage estimator for structural mediation parameters that combines deep representation learning with G-estimation under the "no essential heterogeneity" (NEH) assumption.

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arXiv Machine Learning

Jul 1

MediEncoder: Nonlinear Representation Learning for High-Dimensional Causal Mediation Analysis

arXiv:2606. 30648v1 Announce Type: cross Abstract: Causal mediation analysis decomposes a treatment effect into indirect pathways through mediators and direct pathways not operating through them.

By Shi Bo, Debarghya Mukherjee, AmirEmad Ghassami

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arXiv Machine Learning

Jun 30

Distributional Causal Mediation via Conditional Generative Modeling

arXiv:2605. 01765v2 Announce Type: replace-cross Abstract: Mediation analysis has traditionally focused on outcome-level summary contrasts, such as mean effects, which may obscure substantial distributional changes induced by complex and nonlinear causal mechanisms.

By Jinlun Zhang, Haoneng Huang, Zishu Zhan, Chunquan Ou

arXiv Machine Learning

Jun 30

Multiply Robust Causal Mediation Analysis with Continuous Treatments

arXiv:2105. 09254v4 Announce Type: replace-cross Abstract: In many applications, researchers are interested in the direct and indirect causal effects of a treatment or exposure on an outcome of interest.

By Yizhen Xu, AmirEmad Ghassami, Numair Sani, Ilya Shpitser

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

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arXiv Statistics ML

3d ago

Proximal Balancing for Causal Effect Estimation under Unmeasured Confounding

arXiv:2609.40051v1 Announce Type: new Abstract: Estimating causal effects from observational data is central to science and policy, but the effects are not identified when confounders are unmeasured....

By Yonghan Jung

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arXiv Machine Learning

Sep 4

Causal Foundation Models

Causal Foundation Models (CFMs) are pretrained neural networks designed to estimate causal quantities—such as the average treatment effect—across new datasets using in‑context learning, eliminating the need for bespoke pipelines or model updates. The paper introduces CFMs, reviews foundational concepts in causal inference and machine learning, and provides practical code examples and Jupyter notebooks to illustrate their application.

By Christopher Stith, Hossein Rahmani, Jesse C. Cresswell

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