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The proximal causal inference framework enables the identification and estimation of causal effects in the presence of unmeasured confounding by leveraging two disjoint sets of observed strong proxies: negative control treatments and…

Methodology · Statistics 2025-12-16 Antonio Olivas-Martinez , Peter B. Gilbert , Andrea Rotnitzky

Discovering and parameterising latent confounders represent important and challenging problems in causal structure learning and density estimation respectively. In this paper, we focus on both discovering and learning the distribution of…

Machine Learning · Computer Science 2022-08-23 Kiattikun Chobtham , Anthony C. Constantinou

Recent developments in causal machine learning methods have made it easier to estimate flexible relationships between confounders, treatments and outcomes, making unconfoundedness assumptions in causal analysis more palatable. How…

Econometrics · Economics 2026-05-22 Justin Young , Eleanor Wiske Dillon

Mediation analysis aims at disentangling the effects of a treatment on an outcome through alternative causal mechanisms and has become a popular practice in biomedical and social science applications. The causal framework based on…

Methodology · Statistics 2024-09-27 Allan Jerolon , Laura Baglietto , Etienne Birmele , Vittorio Perduca , Flora Alarcon

Estimating causal effects under networked interference from observational data is a crucial yet challenging problem. Most existing methods mainly rely on the networked unconfoundedness assumption, which guarantees the identification of…

Machine Learning · Computer Science 2026-01-28 Weilin Chen , Ruichu Cai , Jie Qiao , Yuguang Yan , José Miguel Hernández-Lobato

In real-world studies, the collected confounders may suffer from measurement error. Although mismeasurement of confounders is typically unintentional -- originating from sources such as human oversight or imprecise machinery -- deliberate…

Methodology · Statistics 2024-09-20 Jeffrey Zhang , Junu Lee

Observational studies of causal effects require adjustment for confounding factors. In the tabular setting, where these factors are well-defined, separate random variables, the effect of confounding is well understood. However, in public…

Machine Learning · Computer Science 2023-02-16 Connor T. Jerzak , Fredrik Johansson , Adel Daoud

Causal inference relies on the untestable assumption of no unmeasured confounding. Sensitivity analysis can be used to quantify the impact of unmeasured confounding on causal estimates. Among sensitivity analysis methods proposed in the…

Methodology · Statistics 2026-03-12 Yushu Zou , Liangyuan Hu , Amanda Ricciuto , Mark Deneau , Kuan Liu

Causal discovery and causal effect estimation are two fundamental tasks in causal inference. While many methods have been developed for each task individually, statistical challenges arise when applying these methods jointly: estimating…

Methodology · Statistics 2024-08-21 Paula Gradu , Tijana Zrnic , Yixin Wang , Michael I. Jordan

Causal mediation analysis is used to evaluate direct and indirect causal effects of a treatment on an outcome of interest through an intermediate variable or a mediator.It is difficult to identify the direct and indirect causal effects…

Applications · Statistics 2020-01-14 Wei Li , Chunchen Liu , Zhi Geng , John Murray

In recommender systems, various latent confounding factors (e.g., user social environment and item public attractiveness) can affect user behavior, item exposure, and feedback in distinct ways. These factors may directly or indirectly…

Information Retrieval · Computer Science 2025-10-28 Zhirong Huang , Shichao Zhang , Debo Cheng , Jiuyong Li , Lin Liu , Guixian Zhang

Identifying causal effects in the presence of unmeasured variables is a fundamental challenge in causal inference, for which proxy variable methods have emerged as a powerful solution. We contrast two major approaches in this framework: (1)…

Methodology · Statistics 2026-05-12 Helen Guo , Elizabeth L. Ogburn , Ilya Shpitser

Clinical machine learning applications are often plagued with confounders that are clinically irrelevant, but can still artificially boost the predictive performance of the algorithms. Confounding is especially problematic in mobile health…

Applications · Statistics 2018-11-29 Elias Chaibub Neto

Estimating average causal effect (ACE) is useful whenever we want to know the effect of an intervention on a given outcome. In the absence of a randomized experiment, many methods such as stratification and inverse propensity weighting have…

Machine Learning · Computer Science 2019-07-11 Rathin Desai , Amit Sharma

Confounder selection, namely choosing a set of covariates to control for confounding between a treatment and an outcome, is arguably the most important step in the design of an observational study. Previous methods, such as Pearl's…

Methodology · Statistics 2026-03-24 F. Richard Guo , Qingyuan Zhao

Estimating the effect of intervention from observational data while accounting for confounding variables is a key task in causal inference. Oftentimes, the confounders are unobserved, but we have access to large amounts of additional…

Machine Learning · Computer Science 2022-12-13 Shachi Deshpande , Kaiwen Wang , Dhruv Sreenivas , Zheng Li , Volodymyr Kuleshov

Representation learning assumes that real-world data is generated by a few semantically meaningful generative factors (i.e., sources of variation) and aims to discover them in the latent space. These factors are expected to be causally…

Machine Learning · Computer Science 2023-10-27 Xiaoyu Liu , Jiaxin Yuan , Bang An , Yuancheng Xu , Yifan Yang , Furong Huang

Recent approaches to causal inference have focused on causal effects defined as contrasts between the distribution of counterfactual outcomes under hypothetical interventions on the nodes of a graphical model. In this article we develop…

Methodology · Statistics 2023-04-26 Iván Díaz

Estimating conditional means using only the marginal means available from aggregate data is commonly known as the ecological inference problem (EI). We provide a reassessment of EI, including a new formalization of identification conditions…

Applications · Statistics 2026-01-13 Shiro Kuriwaki , Cory McCartan

Causal models communicate our assumptions about causes and effects in real-world phe- nomena. Often the interest lies in the identification of the effect of an action which means deriving an expression from the observed probability…

Machine Learning · Statistics 2018-06-20 Santtu Tikka , Juha Karvanen