arXiv Machine Learning

The Spectral Structure of Latent Treatment Effects

arXiv:2607. 10926v1 Announce Type: new Abstract: Identifying heterogeneous treatment effects under unobserved confounding is central in observational causal inference.

arXiv Machine Learning

Jul 17

Causal Inference for Sequential Settings under Interference and Latent Confounding

arXiv:2607. 14940v1 Announce Type: new Abstract: We study causal inference under outcome interference for sequential, observational settings.

By Phevos Paschalidis, Constantinos Daskalakis, Devavrat Shah

arXiv Machine Learning

Aug 3

Nonparametric Partial Disentanglement via Mechanism Sparsity: Sparse Actions, Interventions and Sparse Temporal Dependencies

arXiv:2401. 04890v2 Announce Type: replace-cross Abstract: This work introduces a novel principle for disentanglement we call mechanism sparsity regularization, which applies when the latent factors of interest depend sparsely on observed auxiliary variables and/or past latent factors.

By S\'ebastien Lachapelle, Pau Rodr\'iguez L\'opez, Yash Sharma, Katie Everett, R\'emi Le Priol, Alexandre Lacoste, Simon Lacoste-Julien

efficiency

arXiv Machine Learning

Aug 12

Causal Variational Deep Embedding: A Family of Interventional Generators for Confounded Images

arXiv:2606. 21806v2 Announce Type: replace Abstract: Deep generative models reproduce the observational distribution of their training data, inheriting any spurious associations it contains.

By Jingyuan Chen, Kangrui Ruan, Junzhe Zhang

rag 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 Machine Learning

Jun 11

Point-Identification of a Robust Predictor Under Latent Shift with Imperfect Proxies

arXiv:2603. 15158v2 Announce Type: replace Abstract: Addressing the domain adaptation problem becomes more challenging when distribution shifts across domains stem from latent confounders that affect both covariates and outcomes.

By Zahra Rahiminasab, Reza Soumi, Arto Klami, Samuel Kaski

fine-tuning

arXiv Machine Learning

Jun 16

From Tokens to Policy: Causal and Interpretable Heterogeneous Treatment Effects Identification

arXiv:2606. 17010v1 Announce Type: new Abstract: Heterogeneous Treatment Effect (HTE) identification is crucial to explain the impact of an intervention and optimize our policies accordingly.

By Riccardo Cadei, Frank Otchere, Nyasha Tirivayi, Gustavo Angeles Tagliaferro, Falco J. Bargagli-Stoffi, Francesco Locatello

multimodal safety

arXiv Machine Learning

Sep 1

Jigsaw-CRL: Recovering Global Latent Causal Order from Fragmented Multi-Client Interventions

arXiv:2608.28991v1 Announce Type: cross Abstract: Causal representation learning (CRL) aims to recover latent causal variables and their structural relations from high-dimensional observations. Exist...

By Haijie Xu, Chen Zhang

arXiv Machine Learning

Jul 21

Unveiling Invariant and Transferable Latent Factors Across Heterogeneous Environments via ATLAS

arXiv:2607. 18209v1 Announce Type: cross Abstract: This paper considers a multi-environment factor model in which high-dimensional covariates are collected from heterogeneous environments, with auxiliary labels available in a subset of these environments.

By Yihong Gu, Katherine Liao, Tianxi Cai

fine-tuning safety

arXiv Statistics ML

3d ago

Synthetic Nearest Neighbors: Extending Synthetic Controls for Matrix Completion with Missing Not at Random Data

arXiv:2609.13586v1 Announce Type: cross Abstract: We develop a causal framework for matrix completion under missing not at random (MNAR) data. Drawing on synthetic controls from the econometric panel...

By Anish Agarwal, Munther Dahleh, Devavrat Shah, Dennis Shen

arXiv Machine Learning

Jun 2

Frequentist Consistency of Prior-Data Fitted Networks for Causal Inference

arXiv:2603. 12037v2 Announce Type: replace Abstract: Foundation models based on prior-data fitted networks (PFNs) have shown strong empirical performance in causal inference by framing the task as an in-context learning problem.

By Valentyn Melnychuk, Vahid Balazadeh, Stefan Feuerriegel, Rahul G. Krishnan

llms safety

arXiv Machine Learning

Aug 24

Regression-Based Estimation of Causal Effects in the Presence of Selection Bias and Confounding

arXiv:2503. 20546v2 Announce Type: replace-cross Abstract: We consider the problem of estimating the expected causal effect $E[Y|do(X)]$ for a target variable $Y$ when treatment $X$ is set by intervention, focusing on continuous random variables.

By Marlies Hafer, Alexander Marx

safety

arXiv Statistics ML

Aug 24

Let Time Tell: Identification and Gaussian Process Estimation for Interrupted Time Series

arXiv:2608.20610v1 Announce Type: cross Abstract: We study causal inference in interrupted time series designs where a treatment affects every unit simultaneously, so that the contemporaneous control...

By Soonhong Cho

safety

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