The paper tackles the problem of estimating causal effects when an unobserved confounder is present. It assumes a single, possibly multi‑dimensional proxy variable for the confounder and knowledge of the mechanism that generates this proxy. Under the Single Proxy Identifiability of Causal Effects (SPICE) assumption, the authors prove that the error mechanism is complete and causal effects are identifiable, extending prior proxy‑based results to continuous, multi‑dimensional settings and more flexible functional forms. They also introduce SPICE‑Net, a neural‑network‑based framework for estimating causal effects applicable to both discrete and continuous treatments.
By Silvan Vollmer, Niklas Pfister, Sebastian Weichwald
The paper introduces a mixture‑learning framework for causal inference with unobserved confounding, treating latent confounders as sources of heterogeneity that create mixture structures in observed data. By assuming suitable structural and identifiability conditions, it shows that recovering the mixing distribution and component mechanisms allows estimation of interventional distributions and causal estimands. The authors illustrate the approach with Bernoulli mixture examples, extend it to high‑dimensional exponential‑family mixtures with dependent outcomes, and relate it to panel‑data settings, latent factor models, and synthetic interventions.
By Mansi Sood, Devavrat Shah
arXiv:2609.36881v1 Announce Type: new
Abstract: Causal foundation models (CFMs) pre-trained on data generated from various structural causal models (SCMs) have been proposed for estimating causal eff...
By Heejin Jung, Gyeongdeok Seo, Hoyoon Byun, Joseph Lee, Kyungwoo Song
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
The paper presents a method for partially identifying counterfactual queries without requiring a fully specified causal graph. By exploiting the topological ordering implied by the query itself, the authors transform the identification problem into a linear programming task, enabling bounds on arbitrary counterfactual and nested counterfactual queries. They demonstrate the tightness of these bounds and illustrate the approach on several case studies, showing its usefulness even when causal knowledge is incomplete.
By Eric Rossetto, Alessandro Antonucci
arXiv:2603. 02159v2 Announce Type: replace-cross Abstract: Instrumental variable (IV) and proximal causal learning (Proxy) methods are central frameworks for causal inference in the presence of unobserved confounding.
By Yuqi Zhang, Krikamol Muandet, Dino Sejdinovic, Edwin Fong, Siu Lun Chau
arXiv:2603.08682v3 Announce Type: replace-cross
Abstract: We introduce structural causal bottleneck models (SCBMs), a novel class of structural causal models in which causal effects between high-dime...
By Simon Bing, Jonas Wahl, Jakob Runge
arXiv:2610.19211v1 Announce Type: cross
Abstract: While causal discovery methods have matured for complete data, such algorithms for handling missing values remain limited. We establish general suffi...
By Matteo Ceriscioli, Shohei Shimizu, Mallika Gupta, Karthika Mohan
arXiv:2610.23543v1 Announce Type: cross
Abstract: Learning causal structure from data requires assumptions about mechanisms, noises, observation schemes, and interventions. Historically, causal disco...
By Yang Ni
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
arXiv:2602. 14972v2 Announce Type: replace Abstract: Estimating causal quantities traditionally relies on bespoke estimators tailored to specific assumptions.
By Arik Reuter, Anish Dhir, Cristiana Diaconu, Jake Robertson, Ole Ossen, Frank Hutter, Adrian Weller, Mark van der Wilk, Bernhard Sch\"olkopf
The paper proposes supervised learning objectives for estimating causal effects of high‑dimensional treatments, such as text or continuous variables, by recasting causal inference as a learning problem under realizability and no unobserved confounding. It shows that causal error can be decomposed into moment‑balancing errors of increasing order and designs objectives that directly improve causal estimation by exploiting the structure of conditional outcomes. The authors also demonstrate how to project high‑dimensional treatment effects onto coarser attributes, enabling a single model to answer multiple causal questions without extra attribute‑specific training, and validate their approach empirically across continuous, discrete, and text treatments.
By Nikita Dhawan, Arnav Paruthi, Andrew Kim, Lovedeep Gondara, Jekaterina Novikova, Chris J. Maddison