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Learning Adjustment Sets from Observational and Limited Experimental Data
, 2020 Estimating causal effects from observational data is not always possible due to confounding. Identifying a set of appropriate covariates (adjustment set) and adjusting for their influence can remove confounding bias; however, such a set is typically not ...
Cooper, Gregory, Triantafillou, Sofia
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The Importance of Scale for Spatial-Confounding Bias and Precision of Spatial Regression Estimators [PDF]
, 2009 Residuals in regression models are often spatially correlated. Prominent examples include studies in environmental epidemiology to understand the chronic health effects of pollutants.
Paciorek, Christopher J.
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A note on a sensitivity analysis for unmeasured confounding, and the related E-value
Journal of Causal Inference, 2020 Unmeasured confounding is one of the most important threats to the validity of observational studies. In this paper we scrutinize a recently proposed sensitivity analysis for unmeasured confounding.
Sjölander Arvid
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Scandinavian Journal of Work, Environment & Health, 2022 OBJECTIVE: We aimed to investigate the influence of unobserved individual characteristics in explaining the effects of work-related factors on full (fSA) and part-time sickness absence (pSA). METHODS: We used register-based panel data for the period 2005–
Elli Hartikainen +3 more
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Indian Journal of Psychiatry, 2007 Confounding is a statistical concept that is important to all researchers.The concept of confounding is explained with the help of an amusing but true example. Simple explanations about and examples of confounding are provided. Methods to deal with confounding are detailed and their applications and disadvantages are examined.Attention to confounding ...
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Simple yet sharp sensitivity analysis for unmeasured confounding
Journal of Causal Inference, 2022 We present a method for assessing the sensitivity of the true causal effect to unmeasured confounding. The method requires the analyst to set two intuitive parameters. Otherwise, the method is assumption free. The method returns an interval that contains
Peña Jose M.
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Recovery and inference of causal effects with sequential adjustment for confounding and attrition
Journal of Causal InferenceConfounding bias and selection bias bring two significant challenges to the validity of conclusions drawn from applied causal inference. The latter can stem from informative missingness, such as in cases of attrition.
de Aguas Johan +3 more
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, 2015 A sensitivity analysis in an observational study assesses the robustness of significant findings to unmeasured confounding. While sensitivity analyses in matched observational studies have been well addressed when there is a single outcome variable ...
Fogarty, Colin B., Small, Dylan S.
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Clinical Epidemiology, 2017 Merete Osler,1,2 Ida Kim Wium-Andersen,1,3 Martin Balslev Jørgensen,3 Terese Sara Høj Jørgensen,1,2 Marie Kim Wium-Andersen1 1Research Center for Prevention and Health, Rigshospitalet – Glostrup, Copenhagen University, Glostrup,
Osler M +4 more
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Confounding explanations. . . .
Behavioral and Brain Sciences, 2001 I argue that, while Finlay et al, are correct to suggest that there are developmental regularities (or constraints) acting on brain component evolution, they are incorrect to infer from this that a developmental explanation necessarily implies that structural changes preceded functional use. Developmental and functional (adaptationist) explanations are
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