Literature DB >> 27641316

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

Miguel A Hernán1.   

Abstract

"Can this number be interpreted as a causal effect?" is a key question for scientists and decision makers. The potential outcomes approach, a quantitative counterfactual theory, describes conditions under which the question can be answered affirmatively. This article reviews one of those conditions, known as consistency, and its implications for real world decisions.
Copyright © 2016 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Causal inference; Consistency; Counterfactuals; Potential outcomes; Well-defined interventions

Mesh：

Substances：

Year:  2016        PMID： 27641316      PMCID： PMC5207342          DOI： 10.1016/j.annepidem.2016.08.016

Source DB:  PubMed          Journal:  Ann Epidemiol        ISSN： 1047-2797            Impact factor:   3.797


  30 in total

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

Authors:  Judea Pearl
Journal:  Epidemiology       Date:  2010-11       Impact factor: 4.822

2.  Negative controls: a tool for detecting confounding and bias in observational studies.

Authors:  Marc Lipsitch; Eric Tchetgen Tchetgen; Ted Cohen
Journal:  Epidemiology       Date:  2010-05       Impact factor: 4.822

3.  Invited commentary: hypothetical interventions to define causal effects--afterthought or prerequisite?

Authors:  Miguel A Hernán
Journal:  Am J Epidemiol       Date:  2005-08-24       Impact factor: 4.897

4.  Toward Causal Inference With Interference.

Authors:  Michael G Hudgens; M Elizabeth Halloran
Journal:  J Am Stat Assoc       Date:  2008-06       Impact factor: 5.033

5.  The consistency statement in causal inference: a definition or an assumption?

Authors:  Stephen R Cole; Constantine E Frangakis
Journal:  Epidemiology       Date:  2009-01       Impact factor: 4.822

6.  Does obesity shorten life? The importance of well-defined interventions to answer causal questions.

Authors:  M A Hernán; S L Taubman
Journal:  Int J Obes (Lond)       Date:  2008-08       Impact factor: 5.095

7.  Concerning the consistency assumption in causal inference.

Authors:  Tyler J VanderWeele
Journal:  Epidemiology       Date:  2009-11       Impact factor: 4.822

8.  On causal inference in the presence of interference.

Authors:  Eric J Tchetgen Tchetgen; Tyler J VanderWeele
Journal:  Stat Methods Med Res       Date:  2010-11-10       Impact factor: 3.021

9.  Compound treatments and transportability of causal inference.

Authors:  Miguel A Hernán; Tyler J VanderWeele
Journal:  Epidemiology       Date:  2011-05       Impact factor: 4.822

10.  Epidemiologic measures and policy formulation: lessons from potential outcomes.

Authors:  Sander Greenland
Journal:  Emerg Themes Epidemiol       Date:  2005-05-27
View more
  45 in total

1.  Obtaining Actionable Inferences from Epidemiologic Actions.

Authors:  Ashley I Naimi
Journal:  Epidemiology       Date:  2019-03       Impact factor: 4.822

2.  A causal framework for classical statistical estimands in failure-time settings with competing events.

Authors:  Jessica G Young; Mats J Stensrud; Eric J Tchetgen Tchetgen; Miguel A Hernán
Journal:  Stat Med       Date:  2020-01-27       Impact factor: 2.373

3.  Is the Smog Lifting?: Causal Inference in Environmental Epidemiology.

Authors:  W Dana Flanders; Michael D Garber
Journal:  Epidemiology       Date:  2019-05       Impact factor: 4.822

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.  A definition of the causal effect of a political party's nominee on the U.S. general presidential election using counterfactual response types.

Authors:  Michael D Garber; Lindsay J Collin; W Dana Flanders
Journal:  Ann Epidemiol       Date:  2020-05-12       Impact factor: 3.797

6.  Evaluating Public Health Interventions: 5. Causal Inference in Public Health Research-Do Sex, Race, and Biological Factors Cause Health Outcomes?

Authors:  M Maria Glymour; Donna Spiegelman
Journal:  Am J Public Health       Date:  2016-11-17       Impact factor: 9.308

7.  Explaining intersectionality through description, counterfactual thinking, and mediation analysis.

Authors:  John W Jackson
Journal:  Soc Psychiatry Psychiatr Epidemiol       Date:  2017-05-24       Impact factor: 4.328

8.  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

9.  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

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

Authors:  Miguel A Hernán
Journal:  Am J Public Health       Date:  2018-03-22       Impact factor: 9.308
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.