Confounding-robust policy improvement
We study the problem of learning personalized decision policies from observational data
while accounting for possible unobserved confounding in the data-generating process …
while accounting for possible unobserved confounding in the data-generating process …
Unmeasured confounding in nonrandomized studies: quantitative bias analysis in health technology assessment
TP Leahy, S Kent, C Sammon… - Journal of …, 2022 - becarispublishing.com
Evidence generated from nonrandomized studies (NRS) is increasingly submitted to health
technology assessment (HTA) agencies. Unmeasured confounding is a primary concern …
technology assessment (HTA) agencies. Unmeasured confounding is a primary concern …
Minimax-optimal policy learning under unobserved confounding
We study the problem of learning personalized decision policies from observational data
while accounting for possible unobserved confounding. Previous approaches, which …
while accounting for possible unobserved confounding. Previous approaches, which …
Distributionally robust causal inference with observational data
We consider the estimation of average treatment effects in observational studies and
propose a new framework of robust causal inference with unobserved confounders. Our …
propose a new framework of robust causal inference with unobserved confounders. Our …
Sensitivity analysis for matched observational studies with continuous exposures and binary outcomes
Matching is one of the most widely used study designs for adjusting for measured
confounders in observational studies. However, unmeasured confounding may exist and …
confounders in observational studies. However, unmeasured confounding may exist and …
Evidence factors from multiple, possibly invalid, instrumental variables
Evidence factors from multiple, possibly invalid, instrumental variables Page 1 The Annals of
Statistics 2022, Vol. 50, No. 3, 1266–1296 https://doi.org/10.1214/21-AOS2148 © Institute of …
Statistics 2022, Vol. 50, No. 3, 1266–1296 https://doi.org/10.1214/21-AOS2148 © Institute of …
Sensitivity analysis for quantiles of hidden biases in matched observational studies
D Wu, X Li - Journal of the American Statistical Association, 2024 - Taylor & Francis
Causal conclusions from observational studies may be sensitive to unmeasured
confounding. In such cases, a sensitivity analysis is often conducted, which tries to infer the …
confounding. In such cases, a sensitivity analysis is often conducted, which tries to infer the …
Extended sensitivity analysis for heterogeneous unmeasured confounding with an application to sibling studies of returns to education
CB Fogarty, RB Hasegawa - The Annals of Applied Statistics, 2019 - JSTOR
The conventional model for assessing insensitivity to hidden bias in paired observational
studies constructs a worst-case distribution for treatment assignments subject to bounds on …
studies constructs a worst-case distribution for treatment assignments subject to bounds on …
Model assisted sensitivity analyses for hidden bias with binary outcomes
G Nattino, B Lu - Biometrics, 2018 - academic.oup.com
In medical and health sciences, observational studies are a major data source for inferring
causal relationships. Unlike randomized experiments, observational studies are vulnerable …
causal relationships. Unlike randomized experiments, observational studies are vulnerable …
Comparison of Nearest Neighbor and Caliper Algorithms in Outcome Propensity Score Matching to Study the Relationship between Type 2 Diabetes and Coronary …
Introduction: Propensity score matching (PSM) is a method to reduce the impact of essential
and confounders. When the number of confounders is high, there may be a problem of …
and confounders. When the number of confounders is high, there may be a problem of …