The paper introduces a new framework for identifying average dose-response functions in the presence of unmeasured confounding by using instrumental variables. It defines a uniform regular weighting function and partitions the treatment space into open sets where local identification is possible. For estimation, the authors propose an augmented inverse probability weighted score within a debiased machine learning setting, along with practical guidance for constructing weighting functions, falsification tests for the additive IV condition, and asymptotic theory for kernel regression or empirical risk minimization estimators.
By Shuyuan Chen, Peng Zhang, Yifan Cui
arXiv:2605. 13430v3 Announce Type: replace-cross Abstract: Selection bias is pervasive in observational studies.
By Yiwen Qiu, Filip Kova\v{c}evi\'c, Shimeng Huang, Peter Spirtes, Francesco Locatello
arXiv:2608. 19383v1 Announce Type: cross Abstract: Average dose-response functions are widely used to summarize causal effects of continuous treatments, but most existing methods assume that the observed sample represents the target population.
By Jay Jojo Cheng, Guanhua Chen
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:2606. 07399v1 Announce Type: cross Abstract: Generative models for counterfactual outcomes have great potential to support decision-making under complex interventions, but existing approaches are limited by unstable estimation, poor generalization across environments, and bias from nuisance model misspecification.
By Raphael C Kim, Jingsen Zhu, Ramin Zabih, Michele Santacatterina
The paper introduces a new estimand for conditional distributional treatment effects that captures how treatments influence the entire outcome distribution, including variance and tail risks, in a covariate-dependent manner. It presents a doubly robust estimator that is minimax optimal locally and uses it to construct a test for global homogeneity of conditional potential outcome distributions. The test accommodates discrepancies beyond the maximum mean discrepancy, guarantees valid type‑1 error, is consistent against fixed alternatives, and includes a computationally efficient, permutation‑free algorithm with exact closed‑form expressions for two natural discrepancies.
By Saksham Jain, Alex Luedtke
arXiv:2505. 17961v4 Announce Type: replace-cross Abstract: Causal inference typically assumes centralized access to individual-level data.
By R\'emi Khellaf, Aur\'elien Bellet, Julie Josse
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
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
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
arXiv:2606. 03332v1 Announce Type: new Abstract: Probabilistic models are typically trained using task-agnostic objectives like log-loss, which can lead to significant errors in downstream estimation.
By Roman Plaud, Alexandre Perez-Lebel, Antoine Saillenfest, Thomas Bonald, Marine Le Morvan, Ga\"el Varoquaux, Matthieu Labeau
The paper introduces a path‑specific version of the fraction of negatively affected (FNA) to separate total harm into direct and indirect components in causal mediation settings. Because these components depend on joint distributions of potential outcomes that are not point‑identified, the authors develop a partial identification framework, deriving sharp Makarov bounds and a semiparametric efficient estimator with valid confidence intervals under mild margin conditions. The framework is illustrated through numerical experiments, marking the first study of path‑specific harm decomposition and its orthogonal inference.
By Ruizi Yan, Dennis Frauen, Maresa Schr\"oder, Stefan Feuerriegel