Search Results - subject:"Unmeasured confounding"

Proximal reinforcement learning : efficient off-policy evaluation in partially observed markov decision processes

Bennett, Andrew; Kallus, Nathan - In: Operations research 72 (2024) 3, pp. 1071-1086

Persistent link: https://www.econbiz.de/10014557447

Technical Considerations in the Use of the E-Value

VanderWeele, Tyler J.; Ding, Peng; Mathur, Maya - In: Journal of Causal Inference 7 (2019) 2

Abstract The E-value is defined as the minimum strength of association on the risk ratio scale that an unmeasured confounder would have to have with both the exposure and the outcome, conditional on the measured covariates, to explain away the observed exposure-outcome association. We have...

Persistent link: https://www.econbiz.de/10014610871

Propensity Score Weighting for Causal Inference with Clustered Data

Yang, Shu - In: Journal of Causal Inference 6 (2018) 2

Abstract Propensity score weighting is a tool for causal inference to adjust for measured confounders in observational studies. In practice, data often present complex structures, such as clustering, which make propensity score modeling and estimation challenging. In addition, for clustered...

Persistent link: https://www.econbiz.de/10014610867

CAUSAL EFFECT ESTIMATION UNDER LINEAR AND LOG-LINEAR STRUCTURAL NESTED MEAN MODELS IN THE PRESENCE OF UNMEASURED CONFOUNDING

Wang, Chia-Hao - 2010

analyses are biased due to unmeasured confounding. The instrumental variables (IV; Angrist et al., 1996) and G … biased in the presence of unmeasured confounding. Therefore, we consider the G-estimation approach as an alternative solution … unmeasured confounding. …

Persistent link: https://www.econbiz.de/10009439201

A Note on the Control Function Approach with an Instrumental Variable and a Binary Outcome

Tchetgen Tchetgen, Eric - In: Epidemiologic Methods 3 (2014) 1, pp. 107-112

Abstract Unobserved confounding is a well-known threat to causal inference in non-experimental studies. The instrumental variable design can under certain conditions be used to recover an unbiased estimator of a treatment effect even if unobserved confounding cannot be ruled out with certainty....

Persistent link: https://www.econbiz.de/10014590602

Reducing Bias Amplification in the Presence of Unmeasured Confounding through Out-of-Sample Estimation Strategies for the Disease Risk Score

Wyss, Richard; Lunt, Mark; Brookhart, M. Alan; Glynn, … - In: Journal of Causal Inference 2 (2014) 2, pp. 131-146

the presence of unmeasured confounding, controlling for variables that affect treatment (both instrumental variables and … in non-experimental studies. While the DRS has been shown to effectively control for measured confounders, unmeasured … confounding continues to be a fundamental obstacle in non-experimental research. Both theory and simulations have shown that in …

Persistent link: https://www.econbiz.de/10014610811

On parametrization, robustness and sensitivity analysis in a marginal structural Cox proportional hazards model for point exposure

Tchetgen Tchetgen, Eric J.; Robins, James - In: Statistics & Probability Letters 82 (2012) 5, pp. 907-915

proportional hazards marginal structural model for point exposure. Under the key assumption that unmeasured confounding is absent … no unmeasured confounding can seldom be established with certainty with observational data, a second contribution of the … current paper is to propose a general framework for estimation without the assumption of no unmeasured confounding. For this …

Persistent link: https://www.econbiz.de/10011039989