Literature DB >> 20864888

On the consistency rule in causal inference: axiom, definition, assumption, or theorem?

Judea Pearl1.   

Abstract

In 2 recent communications, Cole and Frangakis (Epidemiology. 2009;20:3-5) and VanderWeele (Epidemiology. 2009;20:880-883) conclude that the consistency rule used in causal inference is an assumption that precludes any side-effects of treatment/exposure on the outcomes of interest. They further develop auxiliary notation to make this assumption formal and explicit. I argue that the consistency rule is a theorem in the logic of counterfactuals and need not be altered. Instead, warnings of potential side-effects should be embodied in standard modeling practices that make causal assumptions explicit and transparent.

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Year:  2010        PMID: 20864888     DOI: 10.1097/EDE.0b013e3181f5d3fd

Source DB:  PubMed          Journal:  Epidemiology        ISSN: 1044-3983            Impact factor:   4.822


  42 in total

1.  Imputation approaches for potential outcomes in causal inference.

Authors:  Daniel Westreich; Jessie K Edwards; Stephen R Cole; Robert W Platt; Sunni L Mumford; Enrique F Schisterman
Journal:  Int J Epidemiol       Date:  2015-07-25       Impact factor: 7.196

2.  Nonparametric Bounds and Sensitivity Analysis of Treatment Effects.

Authors:  Amy Richardson; Michael G Hudgens; Peter B Gilbert; Jason P Fine
Journal:  Stat Sci       Date:  2014-11       Impact factor: 2.901

3.  All your data are always missing: incorporating bias due to measurement error into the potential outcomes framework.

Authors:  Jessie K Edwards; Stephen R Cole; Daniel Westreich
Journal:  Int J Epidemiol       Date:  2015-04-28       Impact factor: 7.196

4.  Methodological Challenges When Studying Distance to Care as an Exposure in Health Research.

Authors:  Ellen C Caniglia; Rebecca Zash; Sonja A Swanson; Kathleen E Wirth; Modiegi Diseko; Gloria Mayondi; Shahin Lockman; Mompati Mmalane; Joseph Makhema; Scott Dryden-Peterson; Kalé Z Kponee-Shovein; Oaitse John; Eleanor J Murray; Roger L Shapiro
Journal:  Am J Epidemiol       Date:  2019-09-01       Impact factor: 4.897

5.  For and Against Methodologies: Some Perspectives on Recent Causal and Statistical Inference Debates.

Authors:  Sander Greenland
Journal:  Eur J Epidemiol       Date:  2017-02-20       Impact factor: 8.082

6.  Nonparametric Bounds for the Risk Function.

Authors:  Stephen R Cole; Michael G Hudgens; Jessie K Edwards; M Alan Brookhart; David B Richardson; Daniel Westreich; Adaora A Adimora
Journal:  Am J Epidemiol       Date:  2019-04-01       Impact factor: 4.897

7.  Extending the sufficient component cause model to describe the Stable Unit Treatment Value Assumption (SUTVA).

Authors:  Sharon Schwartz; Nicolle M Gatto; Ulka B Campbell
Journal:  Epidemiol Perspect Innov       Date:  2012-04-03

8.  Worth the weight: using inverse probability weighted Cox models in AIDS research.

Authors:  Ashley L Buchanan; Michael G Hudgens; Stephen R Cole; Bryan Lau; Adaora A Adimora
Journal:  AIDS Res Hum Retroviruses       Date:  2014-12       Impact factor: 2.205

9.  The Consistency Assumption for Causal Inference in Social Epidemiology: When a Rose is Not a Rose.

Authors:  David H Rehkopf; M Maria Glymour; Theresa L Osypuk
Journal:  Curr Epidemiol Rep       Date:  2016-02-16

10.  Are all biases missing data problems?

Authors:  Chanelle J Howe; Lauren E Cain; Joseph W Hogan
Journal:  Curr Epidemiol Rep       Date:  2015-07-12
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