Estimating causal effects from epidemiological data
Miguel A. Hernán,James M. Robins +1 more
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TLDR
This article reviews a condition that permits the estimation of causal effects from observational data, and two methods—standardisation and inverse probability weighting—to estimate population causal effects under that condition.Abstract:
In ideal randomised experiments, association is causation: association measures can be interpreted as effect measures because randomisation ensures that the exposed and the unexposed are exchangeable. On the other hand, in observational studies, association is not generally causation: association measures cannot be interpreted as effect measures because the exposed and the unexposed are not generally exchangeable. However, observational research is often the only alternative for causal inference. This article reviews a condition that permits the estimation of causal effects from observational data, and two methods -- standardisation and inverse probability weighting -- to estimate population causal effects under that condition. For simplicity, the main description is restricted to dichotomous variables and assumes that no random error attributable to sampling variability exists. The appendix provides a generalisation of inverse probability weighting.read more
Citations
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Constructing Inverse Probability Weights for Marginal Structural Models
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Doubly Robust Estimation of Causal Effects
Michele Jonsson Funk,Daniel Westreich,Chris Wiesen,Til Stürmer,M. Alan Brookhart,Marie Davidian +5 more
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Generalizing Evidence From Randomized Clinical Trials to Target Populations The ACTG 320 Trial
TL;DR: In this article, the authors illustrate a model-based method to standardize observed trial results to a specified target population using a seminal human immunodeficiency virus (HIV) treatment trial, and provide Monte Carlo simulation evidence supporting the method.
Book ChapterDOI
Estimation of the causal effects of time-varying exposures
M. Robins James,A. Hernán Migue +1 more
TL;DR: This book contains information obtained from authentic and highly regarded sources and the authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained.
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Bias formulas for sensitivity analysis of unmeasured confounding for general outcomes, treatments, and confounders
TL;DR: The potential outcomes framework is used to derive a general class of sensitivity-analysis formulas for outcomes, treatments, and measured and unmeasured confounding variables that may be categorical or continuous.
References
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A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect
TL;DR: A graphical approach to the identification and computation of causal parameters in mortality studies with sustained exposure periods is offered and an adverse effect of arsenic exposure on all-cause and lung cancer mortality which standard methods fail to detect is found.
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