1932

Abstract

Assessing the extent to which public health research findings can be causally interpreted continues to be a critical endeavor. In this symposium, we invited several researchers to review issues related to causal inference in social epidemiology and environmental science and to discuss the importance of external validity in public health. Together, this set of articles provides an integral overview of the strengths and limitations of applying causal inference frameworks and related approaches to a variety of public health problems, for both internal and external validity.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-publhealth-111918-103312
2019-04-01
2024-06-14
Loading full text...

Full text loading...

/deliver/fulltext/publhealth/40/1/annurev-publhealth-111918-103312.html?itemId=/content/journals/10.1146/annurev-publhealth-111918-103312&mimeType=html&fmt=ahah

Literature Cited

  1. 1. 
    Bareinboim E, Pearl J 2013. A general algorithm for deciding transportability of experimental results. J. Causal Inference 1:107–34
    [Google Scholar]
  2. 2. 
    Bind M-A 2019. Causal modeling in environmental health. Annu. Rev. Public Health 40:23–43
    [Google Scholar]
  3. 3. 
    Bind M-A, Vanderweele TJ, Coull BA, Schwartz JD 2016. Causal mediation analysis for longitudinal data with exogenous exposure. Biostatistics 17:122–34
    [Google Scholar]
  4. 4. 
    Cole SR, Stuart EA 2010. Generalizing evidence from randomized clinical trials to target populations: the ACTG 320 trial. Am. J. Epidemiol. 172:107–15
    [Google Scholar]
  5. 5. 
    Ford CL, Harawa NT 2010. A new conceptualization of ethnicity for social epidemiologic and health equity research. Soc. Sci. Med. 71:251–58
    [Google Scholar]
  6. 6. 
    Galea S, Vaughan R 2016. A public health of consequence. Am. J. Public Health 106:10–11
    [Google Scholar]
  7. 7. 
    Glymour MM, Spiegelman D 2017. Evaluating public health interventions: 5. Causal inference in public health research—Do sex, race, and biological factors cause health outcomes?. Am. J. Public Health 107:81–85
    [Google Scholar]
  8. 8. 
    Green LW, Nasser M 2018. Furthering dissemination and implementation research: the need for more attention to external validity. Dissemination and Implementation Research: Translating Science to Practice R Brownson, G Colditz, E Parker 305–26 New York: Oxford Univ. Press, 2nd ed..
    [Google Scholar]
  9. 9. 
    Haber N, Smith ER, Moscoe E, Andrews K, Audy R et al. 2018. Causal language and strength of inference in academic and media articles shared in social media (CLAIMS): a systematic review. PLOS ONE 13:e0196346
    [Google Scholar]
  10. 10. 
    Hernán MA 2016. Does water kill? A call for less casual causal inferences. Ann. Epidemiol. 26:674–80
    [Google Scholar]
  11. 11. 
    Hernán MA 2018. The C-word: Scientific euphemisms do not improve causal inference from observational data. Am. J. Public Health 108:616–19
    [Google Scholar]
  12. 12. 
    Hernán MA, Robins JM 2006. Estimating causal effects from epidemiological data. J. Epidemiol. Community Health 60:578–86
    [Google Scholar]
  13. 13. 
    Huebschmann AG, Leavitt IM, Glasgow RE 2019. Making health research matter: a call to increase attention to external validity. Annu. Rev. Public Health 40:45–63
    [Google Scholar]
  14. 14. 
    Kaufman J 2019. Commentary: Causal inference for social exposures. Annu. Rev. Public Health 40:7–21
    [Google Scholar]
  15. 15. 
    Krieger N, Davey Smith G 2016. The tale wagged by the DAG: broadening the scope of causal inference and explanation for epidemiology. Int. J. Epidemiol. 45:1787–808
    [Google Scholar]
  16. 16. 
    Robins JM, Weissman MB 2016. Commentary: Counterfactual causation and streetlamps: What is to be done?. Int. J. Epidemiol. 45:1830–35
    [Google Scholar]
  17. 17. 
    Sen M, Wasow O 2016. Race as a bundle of sticks: designs that estimate effects of seemingly immutable characteristics. Annu. Rev. Political Sci. 19:499–522
    [Google Scholar]
  18. 18. 
    Vandenbroucke JP, Broadbent A, Pearce N 2016. Causality and causal inference in epidemiology: the need for a pluralistic approach. Int. J. Epidemiol. 45:1776–86
    [Google Scholar]
  19. 19. 
    VanderWeele TJ, Robinson WR 2014. On the causal interpretation of race in regressions adjusting for confounding and mediating variables. Epidemiology 25:473–84
    [Google Scholar]
  20. 20. 
    Westreich D, Edwards JK, Lesko CR, Cole SR, Stuart EA 2018. Target validity and the hierarchy of study designs. Am. J. Epidemiol. https://doi.org/10.1093/aje/kwy228
    [Crossref] [Google Scholar]
  21. 21. 
    Westreich D, Edwards JK, Lesko CR, Stuart E, Cole SR 2017. Transportability of trial results using inverse odds of sampling weights. Am. J. Epidemiol. 186:1010–14
    [Google Scholar]
/content/journals/10.1146/annurev-publhealth-111918-103312
Loading
  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error