Výsledky vyhledávání - "Guo, Anna A."

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Average Causal Effect Estimation in DAGs with Hidden Variables: Extensions of Back-Door and Front-Door Criteria

Autor: Guo, Anna, Nabi, Razieh

The identification theory for causal effects in directed acyclic graphs (DAGs) with hidden variables is well-developed, but methods for estimating and inferring functionals beyond the g-formula remain limited. Previous studies have proposed semiparam

Externí odkaz: http://arxiv.org/abs/2409.03962

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Targeted Machine Learning for Average Causal Effect Estimation Using the Front-Door Functional

Autor: Guo, Anna, Benkeser, David, Nabi, Razieh

Evaluating the average causal effect (ACE) of a treatment on an outcome often involves overcoming the challenges posed by confounding factors in observational studies. A traditional approach uses the back-door criterion, seeking adjustment sets to bl

Externí odkaz: http://arxiv.org/abs/2312.10234

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Sufficient Identification Conditions and Semiparametric Estimation under Missing Not at Random Mechanisms

Autor: Guo, Anna, Zhao, Jiwei, Nabi, Razieh

Publikováno v: Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI), 2023

Conducting valid statistical analyses is challenging in the presence of missing-not-at-random (MNAR) data, where the missingness mechanism is dependent on the missing values themselves even conditioned on the observed data. Here, we consider a MNAR m

Externí odkaz: http://arxiv.org/abs/2306.06443

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