Search Results - subject:"confounding"

Long-term Exposure to Ambient PM2.5 and Population Health: Evidence from Longitudinally-linked Census Data

Rowland, Neil; McVicar, Duncan; Vlachos, Stavros; … - 2024

Extensive evidence shows exposure to ambient PM2.5 is associated with a wide range of poor health outcomes. But few studies examine genuinely long-run pollution exposures in nationally representative data. This study does so, exploiting longitudinally-linked Census data for Northern Ireland,...

Persistent link: https://www.econbiz.de/10014455128

Correcting regressor-endogeneity bias via instrument-free joint estimation using semiparametric odds ratio models

Qian, Yi; Xie, Hui - In: Journal of marketing research 61 (2024) 5, pp. 914-936

Persistent link: https://www.econbiz.de/10015168502

Long-term exposure to ambient PM2.5 and population health : evidence from longitudinally-linked census data

Rowland, Neil; McVicar, Duncan; Vlachos, Stavros; … - 2024

Persistent link: https://www.econbiz.de/10014456212

Probing for omitted variable bias : the role of the impact threshold of a confounding variable in complementing instrumental variable estimations

Bendig, David; Hoke, Jonathan - In: Industrial marketing management : the international … 122 (2024), pp. 145-159

Persistent link: https://www.econbiz.de/10015125067

Covariate benchmarking for sensitivity analysis when the confounder is correlated with observed covariates

Basu, Deepankar - 2023

Covariate benchmarking is an important part of sensitivity analysis about omitted variable bias and can be used to bound the strength of the unobserved confounder using information and judgments about observed covariates. It is common to carry out formal covariate benchmarking under the...

Persistent link: https://www.econbiz.de/10014480652

Formal covariate benchmarking to bound omitted variable bias

Basu, Deepankar - 2023

Persistent link: https://www.econbiz.de/10014480725

Formal covariate benchmarking to bound omitted variable bias

Basu, Deepankar - 2023

Persistent link: https://www.econbiz.de/10014430784

Covariate benchmarking for sensitivity analysis when the confounder is correlated with observed covariates

Basu, Deepankar - 2023

Persistent link: https://www.econbiz.de/10014292518

Analysis of the effects of adjusting for binary non-confounders in a logistic regression model after all true confounders have been accounted for: A simulation study

Moret, Ravan; Chapple, Andrew G. - 2022

In observational studies, confounding variables that affect both the exposure and an outcome of interest are a general … concern. It is well known that failure to control for confounding variables adequately can worsen inference on an exposure …'s effect on outcome. In this paper, we explore how exposure effect inference changes when non-confounding covariates are added …

Persistent link: https://www.econbiz.de/10014304801

A generalized double robust Bayesian model averaging approach to causal effect estimation with application to the study of osteoporotic fractures

Talbot, Denis; Beaudoin, Claudia - In: Journal of Causal Inference 10 (2022) 1, pp. 335-371

Abstract Analysts often use data-driven approaches to supplement their knowledge when selecting covariates for effect estimation. Multiple variable selection procedures for causal effect estimation have been devised in recent years, but additional developments are still required to adequately...

Persistent link: https://www.econbiz.de/10014610915