Literature DB >> 29565659

The C-Word: Scientific Euphemisms Do Not Improve Causal Inference From Observational Data.

Miguel A Hernán1.   

Abstract

Causal inference is a core task of science. However, authors and editors often refrain from explicitly acknowledging the causal goal of research projects; they refer to causal effect estimates as associational estimates. This commentary argues that using the term "causal" is necessary to improve the quality of observational research. Specifically, being explicit about the causal objective of a study reduces ambiguity in the scientific question, errors in the data analysis, and excesses in the interpretation of the results.

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Year:  2018        PMID: 29565659      PMCID: PMC5888052          DOI: 10.2105/AJPH.2018.304337

Source DB:  PubMed          Journal:  Am J Public Health        ISSN: 0090-0036            Impact factor:   9.308


  14 in total

1.  Re: "Associations are not effects".

Authors:  D A Savitz
Journal:  Am J Epidemiol       Date:  1991-08-15       Impact factor: 4.897

2.  Commentary: Counterfactual causation and streetlamps: what is to be done?

Authors:  James M Robins; Michael B Weissman
Journal:  Int J Epidemiol       Date:  2016-12-01       Impact factor: 7.196

3.  The table 2 fallacy: presenting and interpreting confounder and modifier coefficients.

Authors:  Daniel Westreich; Sander Greenland
Journal:  Am J Epidemiol       Date:  2013-01-30       Impact factor: 4.897

4.  Commentary: A structural approach to Berkson's fallacy and a guide to a history of opinions about it.

Authors:  Jaapjan D Snoep; Alfredo Morabia; Sonia Hernández-Díaz; Miguel A Hernán; Jan P Vandenbroucke
Journal:  Int J Epidemiol       Date:  2014-02-28       Impact factor: 7.196

5.  The birth weight "paradox" uncovered?

Authors:  Sonia Hernández-Díaz; Enrique F Schisterman; Miguel A Hernán
Journal:  Am J Epidemiol       Date:  2006-08-24       Impact factor: 4.897

6.  Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available.

Authors:  Miguel A Hernán; James M Robins
Journal:  Am J Epidemiol       Date:  2016-03-18       Impact factor: 4.897

7.  There is no virtue in vagueness: Comment on: Causal Identification: A Charge of Epidemiology in Danger of Marginalization by Sharon Schwartz, Nicolle M. Gatto, and Ulka B. Campbell.

Authors:  Jay S Kaufman
Journal:  Ann Epidemiol       Date:  2016-08-31       Impact factor: 3.797

8.  The role of model selection in causal inference from nonexperimental data.

Authors:  J M Robins; S Greenland
Journal:  Am J Epidemiol       Date:  1986-03       Impact factor: 4.897

9.  Control of confounding in the assessment of medical technology.

Authors:  S Greenland; R Neutra
Journal:  Int J Epidemiol       Date:  1980-12       Impact factor: 7.196

Review 10.  Does water kill? A call for less casual causal inferences.

Authors:  Miguel A Hernán
Journal:  Ann Epidemiol       Date:  2016-08-31       Impact factor: 3.797

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  64 in total

1.  Value-Based Care for Musculoskeletal Pain: Are Physical Therapists Ready to Deliver?

Authors:  Trevor A Lentz; Adam P Goode; Charles A Thigpen; Steven Z George
Journal:  Phys Ther       Date:  2020-04-17

2.  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

3.  A Clarification on Causal Questions: We Ask Them More Often Than We Realize.

Authors:  Katrina L Kezios; Eleanor Hayes-Larson
Journal:  Am J Public Health       Date:  2018-08       Impact factor: 9.308

4.  The Epidemiologic Toolbox: Identifying, Honing, and Using the Right Tools for the Job.

Authors:  Catherine R Lesko; Alexander P Keil; Jessie K Edwards
Journal:  Am J Epidemiol       Date:  2020-06-01       Impact factor: 4.897

5.  The Critical Importance of Asking Good Questions: The Role of Epidemiology Doctoral Training Programs.

Authors:  Matthew P Fox; Jessie K Edwards; Robert Platt; Laura B Balzer
Journal:  Am J Epidemiol       Date:  2020-04-02       Impact factor: 4.897

6.  In Pursuit of Evidence in Air Pollution Epidemiology: The Role of Causally Driven Data Science.

Authors:  Marco Carone; Francesca Dominici; Lianne Sheppard
Journal:  Epidemiology       Date:  2020-01       Impact factor: 4.822

7.  The Correspondence Between Causal and Traditional Mediation Analysis: the Link Is the Mediator by Treatment Interaction.

Authors:  David P MacKinnon; Matthew J Valente; Oscar Gonzalez
Journal:  Prev Sci       Date:  2020-02

8.  Causal Inference in Environmental Epidemiology: Old and New Approaches.

Authors:  Neil Pearce; Jan P Vandenbroucke; Deborah A Lawlor
Journal:  Epidemiology       Date:  2019-05       Impact factor: 4.822

9.  Calculating Versus Estimating Causal Effects.

Authors:  Michael J Green
Journal:  Am J Public Health       Date:  2018-08       Impact factor: 9.308

10.  A Unification of Mediator, Confounder, and Collider Effects.

Authors:  David P MacKinnon; Sophia J Lamp
Journal:  Prev Sci       Date:  2021-06-23
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