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20122026
most citedSemiparametric theory for causal mediation analysis: Efficiency bounds, multiple robustness and sensitivity analysis

294 citations · 700 across the 36 of their papers we have counts for

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29 papers · 1 filter

stat.ME2026

Functional Estimation under Proxy-Based Full-Law Identification

Helen Guo, AmirEmad Ghassami, Ilya Shpitser +1

We state general conditions under which the full-data law is identified in the presence of latent variables, leveraging key observed variables ("proxies") associated with unobserve…

stat.ME2026

A Characterization of the Orthocomplement of the Tangent Space of Semiparametric Markov Models

Trung Phung, Ilya Shpitser

Graphical models are ubiquitous in social and empirical science as they are intuitive and easy to use. These models belong to the broader class of Markov models, defined using sole…

stat.ME2026

Proximal Identification and Estimation in Front-Door Causal Structures with Unobserved Confounding of the Mediator

Helen Guo, Beatrix Yaxin Wen, Ilya Shpitser

Unobserved confounding is a fundamental obstacle in causal inference problems. In the graphical modeling literature, a general theory has been developed that allows identification…

stat.ME2026

Exploiting independence constraints for efficient estimation of bounds on causal effects in the presence of unmeasured confounding

Ting-Hsuan Chang, Caleb H. Miles, Ilya Shpitser +2

Causal graphs may inform covariate adjustment for estimating causal effects and improve estimation efficiency by exploiting the graphical structure. In many applications, however,…

stat.ME2026

Proximal Causal Inference for Hidden Outcomes

Helen Guo, AmirEmad Ghassami, Ilya Shpitser +1

Methods that rely on proxies, without imposing strong parametric structure, are increasingly used to deal with unobserved variables in causal inference. One influential line of thi…

stat.ME2025

Comparing Two Proxy Methods for Causal Identification

Helen Guo, Elizabeth L. Ogburn, Ilya Shpitser

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 solut…