arXiv Machine Learning July 1, 2026 By Shi Bo, Debarghya Mukherjee, AmirEmad Ghassami

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

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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.

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arXiv Machine Learning

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Representation Learning for Semiparametric Causal Mediation Analysis under No Essential Heterogeneity

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.

By Roberto Faleh, Sofia Morelli, Holger Brandt

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arXiv Statistics ML

Aug 25

Neuro-Causal Factor Analysis

Neuro-Causal Factor Analysis (NCFA) reimagines traditional factor analysis by integrating causal structure learning and deep generative modeling. The method learns a directed graph linking latent and observed variables, then trains a deep generative model that respects the graph’s Markov factorization. Experiments on synthetic and real datasets show NCFA achieves lower reconstruction error than standard FA and better latent distribution recovery than a variational autoencoder, while offering a sparser architecture, reduced complexity, and causal interpretability.

By Alex Markham, Mingyu Liu, Bryon Aragam, Liam Solus

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arXiv Machine Learning

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Distributional Causal Mediation via Conditional Generative Modeling

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

arXiv Machine Learning

Jun 9

Causal Representation Learning from Network Data

arXiv:2509. 01916v2 Announce Type: replace Abstract: Causal disentanglement from soft interventions is identifiable under the assumptions of linear interventional faithfulness and availability of both observational and interventional data.

By Jifan Zhang, Michelle M. Li, Elena Zheleva

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 AI

Jun 24

RetiSEM: Generalising Causal Models for Fragmented Biomedical Data

arXiv:2606. 24488v1 Announce Type: cross Abstract: Learning causal models from fragmented biomedical data is challenging because clinical, molecular, and imaging variables are often incomplete or not jointly observed.

By Inam Ullah, Imran Razzak, Shoaib Jameel

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