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Epidemiology by DesignA Causal Approach to the Health Sciences$
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Daniel Westreich

Print publication date: 2019

Print ISBN-13: 9780190665760

Published to Oxford Scholarship Online: December 2019

DOI: 10.1093/oso/9780190665760.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 17 June 2021

Causal Inference, Causal Effect Estimation, and Systematic Error

Causal Inference, Causal Effect Estimation, and Systematic Error

(p.41) 3 Causal Inference, Causal Effect Estimation, and Systematic Error
Epidemiology by Design

Daniel Westreich

Oxford University Press

Chapter 3 discusses basic concepts in causal inference, beginning with an introduction to potential outcomes and definitions of causal contrasts (or causal estimates of effect), concepts, terms, and notation. Many concepts introduced here will be developed further in subsequent chapters. The author discusses sufficient conditions for estimation of causal effects (which are sometimes called causal identification conditions), causal directed acyclic graphs (sometimes called causal diagrams), and four key types of systematic error (confounding bias, missing data bias, selection bias, and measurement error/information bias). The author also briefly discusses alternative approaches to causal inference.

Keywords:   causal inference, potential outcomes, counterfactuals, exchangeability, positivity, consistency, directed acyclic graphs, confounding, selection bias, missing data

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