The paper introduces a model‑agnostic inference framework for partially identified causal effects that leverages covariate information without requiring discrete covariates or accurate conditional distribution estimates. Using duality theory for optimal transport, the method delivers uniformly valid inference in randomized experiments, is doubly robust in observational settings, achieves asymptotic unbiasedness when nuisance parameters converge semiparametrically, and allows multiplier‑bootstrap selection of covariates and models while remaining computationally efficient. Empirical applications show the approach consistently narrows identified sets and confidence intervals without imposing extra structural assumptions.
By Wenlong Ji, Lihua Lei, Asher Spector
arXiv:2606. 19361v1 Announce Type: cross Abstract: Identification conditions describe the computability of a target query or parameter of interest as a function of the type and amount of information available.
By Lucius E. J. Bynum, Rajesh Ranganath, Kyunghyun Cho
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: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:2601.00287v2 Announce Type: replace-cross
Abstract: The Stable Unit Treatment Value Assumption (SUTVA) includes the condition that there are no multiple versions of treatment in causal inferenc...
By Kohei Yoshikawa, Shuichi Kawano
arXiv:2602. 22083v2 Announce Type: replace-cross Abstract: Causal identification functionals often require integration over conditional densities of continuous variables, such as those arising in nonparametric identification theory of total and mediated causal effects in DAGs with hidden variables.
By Xiaxian Ou, Razieh Nabi
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: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
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
arXiv:2503. 07811v3 Announce Type: replace-cross Abstract: The theory of optimal transportation has developed into a powerful and elegant framework for comparing probability distributions, with wide-ranging applications in all areas of science.
By Florian F Gunsilius
arXiv:2603. 01119v2 Announce Type: replace-cross Abstract: A fundamental challenge in causal inference with observational data is correct specification of a causal model.
By Rohit Bhattacharya, Ina Ocelli, Ted Westling
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