How Effective Are Machine Learning and Doubly Robust Estimators in Incorporating High‐Dimensional Proxies to Reduce Residual Confounding?
Sign Up to like & getrecommendations! Published in 2025 at "Pharmacoepidemiology and Drug Safety"
DOI: 10.1002/pds.70155
Abstract: Residual confounding presents a persistent challenge in observational studies, particularly in high‐dimensional settings. High‐dimensional proxy adjustment methods, such as the high‐dimensional propensity score (hdPS), are widely used to address confounding bias by incorporating proxies for… read more here.
Keywords: dimensional proxies; high dimensional; machine learning; doubly robust ... See more keywords
Doubly robust conditional logistic regression.
Sign Up to like & getrecommendations! Published in 2019 at "Statistics in medicine"
DOI: 10.1002/sim.8332
Abstract: Epidemiologic research often aims to estimate the association between a binary exposure and a binary outcome, while adjusting for a set of covariates (eg, confounders). When data are clustered, as in, for instance, matched case-control… read more here.
Keywords: conditional logistic; logistic regression; robust conditional; odds ratio ... See more keywords
Doubly robust estimation of the causal effects in the causal inference with missing outcome data
Sign Up to like & getrecommendations! Published in 2018 at "Journal of Ambient Intelligence and Humanized Computing"
DOI: 10.1007/s12652-018-0957-2
Abstract: The goal of this article is to attempt to develop doubly robust (DR) estimator in the causal inference with ignorable missing outcome data. In the causal inference with missing outcome data, an estimator is doubly… read more here.
Keywords: outcome data; estimator; causal inference; missing outcome ... See more keywords
Doubly robust difference-in-differences estimators
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Econometrics"
DOI: 10.1016/j.jeconom.2020.06.003
Abstract: Abstract This article proposes doubly robust estimators for the average treatment effect on the treated (ATT) in difference-in-differences (DID) research designs. In contrast to alternative DID estimators, the proposed estimators are consistent if either (but… read more here.
Keywords: differences estimators; proposed estimators; difference differences; doubly robust ... See more keywords
Doubly robust kernel density estimation when group membership is missing at random
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Statistical Planning and Inference"
DOI: 10.1016/j.jspi.2019.09.010
Abstract: Abstract When there are subjects with subpopulation memberships missing, the kernel density estimates of the subpopulations based on the subjects with verified memberships may not be valid unless the missingness of the memberships satisfies the… read more here.
Keywords: missing random; prediction model; kernel density; doubly robust ... See more keywords
Prognostic value of dynamic KDIGO staging in acute kidney injury after acute heart failure: a doubly robust analysis
Sign Up to like & getrecommendations! Published in 2025 at "Scientific Reports"
DOI: 10.1038/s41598-025-07118-y
Abstract: Acute kidney injury (AKI) is a frequent complication in acute heart failure (AHF) patients, yet few studies have examined the prognostic implications of different dynamic KDIGO AKI stages in this population. This retrospective cohort study… read more here.
Keywords: robust analysis; analysis; doubly robust; acute kidney ... See more keywords
Bayesian doubly robust estimation of causal effects for clustered observational data
Sign Up to like & getrecommendations! Published in 2025 at "Journal of Applied Statistics"
DOI: 10.1080/02664763.2024.2449396
Abstract: Observational data often exhibit clustered structure, which leads to inaccurate estimates of exposure effect if such structure is ignored. To overcome the challenges of modelling the complex confounder effects in clustered data, we propose a… read more here.
Keywords: effects clustered; bayesian doubly; observational data; doubly robust ... See more keywords
Double robust variance estimation with parametric working models
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DOI: 10.1093/biomtc/ujaf054
Abstract: ABSTRACT Doubly robust estimators have gained popularity in the field of causal inference due to their ability to provide consistent point estimates when either an outcome or an exposure model is correctly specified. However, for… read more here.
Keywords: working models; parametric working; variance; robust variance ... See more keywords
Doubly robust nonparametric estimators of the predictive value of covariates for survival data.
Sign Up to like & getrecommendations! Published in 2025 at "Biometrics"
DOI: 10.1093/biomtc/ujaf084
Abstract: The predictive value of a covariate is often of interest in studies with a survival endpoint. A common situation is that there are some well established predictors and a potential valuable new marker. The challenge… read more here.
Keywords: value; nonparametric estimators; robust nonparametric; doubly robust ... See more keywords
Continuous Value Assignment: A Doubly Robust Data Augmentation for Off-Policy Learning
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Transactions on Neural Networks and Learning Systems"
DOI: 10.1109/tnnls.2024.3435406
Abstract: Deep reinforcement learning (RL) has witnessed remarkable success in a wide range of control tasks. To overcome RL’s notorious sample inefficiency, prior studies have explored data augmentation techniques leveraging collected transition data. However, these methods… read more here.
Keywords: value; value assignment; doubly robust; continuous value ... See more keywords
Improving causal inference with a doubly robust estimator that combines propensity score stratification and weighting
Sign Up to like & getrecommendations! Published in 2017 at "Journal of Evaluation in Clinical Practice"
DOI: 10.1111/jep.12714
Abstract: RATIONALE, AIMS AND OBJECTIVES When a randomized controlled trial is not feasible, health researchers typically use observational data and rely on statistical methods to adjust for confounding when estimating treatment effects. These methods generally fall… read more here.
Keywords: treatment; estimator; model; propensity score ... See more keywords