Summary of relationships between exchangeability, biasing paths and bias

European Journal of Epidemiology

Volumn 30, Issue 10, 2014, Pages 1089-1099

  Flanders, William Dana ^a   Eldridge, Ronald Curtis ^a  

^a NONE   (United States)

Author keywords

Bias; Biasing path; Causal effect; Confounding; Directed acyclic graph; Exchangeability

Indexed keywords

ALCOHOL CONSUMPTION; ATTRIBUTABLE RISK; CAUSAL MODELING; CONTROLLED STUDY; HEART INFARCTION; POTENTIAL OUTCOME MODEL; PROBABILITY; REVIEW; SELECTION BIAS; SMOKING; STATISTICAL MODEL; STRATIFICATION; EPIDEMIOLOGY; HUMAN; RESEARCH; STATISTICAL ANALYSIS; STATISTICAL BIAS; THEORETICAL MODEL;

BIAS (EPIDEMIOLOGY); CAUSALITY; CONFOUNDING FACTORS (EPIDEMIOLOGY); DATA INTERPRETATION, STATISTICAL; HUMANS; MODELS, STATISTICAL; MODELS, THEORETICAL; RESEARCH;

EID: 84947046558     PISSN: 03932990     EISSN: 15737284     Source Type: Journal    
DOI: 10.1007/s10654-014-9915-2     Document Type: Review

References (27)

1. Rothman KJ, Greenland S, Lash TL. Modern epidemiology. 3rd ed. Philadelphia: Lippincott Williams & Wilkins; 2008.
2. Rothman KJ. Modern epidemiology. Boston: Little, Brown; 1986.
3. Greenland S, Robins J, Pearl J. Confounding and collapsibility in causal inference. Stat Sci. 1999;14:29–46.
4. Miettinen OS, Cook EF. Confounding: essence and detection. Am J Epidemiol. 1981;114:593–603.
5. Greenland S, Robins J. Identifiability, exchangeability, and epidemiologic confounding. Int J Epidemiol. 1986;15:413–9.
6. Greenland S, Brumback B. An overview of relations among causal modelling methods. Int J Epidemiol. 2002;31:1030–7.
7. Greenland S, Robins JM. Identifiability, exchangeability and confounding revisited. Epidemiol Perspect Innov. 2009;6. doi:10.1186/742-5573-6-4.
8. Pearl J. Causal diagrams for empirical research (with discussion). Biometrika. 1995;82:669–710.
9. Pearl J. Some apects of graphical models connected with causality. In: 49th session of the International Statistical Institute, Florence, Italy; 1993.
10. Glymour MM, Greenland S. Modern Epidemiology, 3rd ed. In: Rothman KJ, Greenland S, Lash TL, editors. Causal Diagrams. Philadelphia: Lippincott, Williams & Wilkins; 2008. p. 183–209.
11. Greenland S, Pearl J, Robins J. Causal diagrams for epidemiologic research. Epidemiology. 1999;10:37–48.
12. Greenland S. Quantifying biases in causal models: classical confounding vs collider-stratification bias. Epidemiology. 2003;14:300–6.
13. Pearl J. Causality. 2nd ed. Cambridge: Cambridge University Press; 2009.
14. Greenland S, Pearl J. Adjustments and their consequences—collapsibility analysis using graphical models. Int Stat Rev. 2011;79:401–26.
15. VanderWeele TJ, Robins JM. Directed acyclic graphs, sufficient causes, and the properties of conditioning on a common effect. Am J Epidemiol. 2007;166:1096–104.
16. Robins JM, Richardson T. Alternative graphical causal models and the identification of direct effects. In: Shrout P, Keyes K, Ornstein K, editors. Causality and psychopathology: finding the determinants of disorders and their cures. Oxford: Oxford University Press; 2010. p. 103–58.
17. Hernan MA, Robins J. Causal inference. 2012 ed; 2012. http://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/. Accessed 1 Oct 2012.
18. Greenland S, Pearl J. Causal diagrams. In: Boslaugh S, editor. Encyclopedia of epidemiology. Thousand Oaks: Sage; 2007. p. 149–56.
19. Rubin DB. Estimating causal effects of treatments in randomized and nonrandomized studies. J Educ Psychol. 1974;66:688–701.
20. Hernán MA, Robins J. A definition of causal effect for epidemiology. J Epidemiol Community Health. 2004;58:265–71.
21. Maldonado G, Greenland S. Estimating causal effects. Int J Epidemiol. 2002;31:422–9.
22. Rubin DB. Comment: Neyman (1923) and causal inference in experiments and observational studies. Stat Sci. 1990;5:472–80.
23. Rubin DB. Direct and indirect causal effects via potential outcomes. Scand J Stat. 2004;31:161–70.
24. Hernán MA, Robins JM. Estimating causal effects from epidemiological data. J Epidemiol Community Health. 2006;60:578–86.
25. VanderWeele TJ. Causal mediation analysis with survival data. Epidemiology (Cambridge, Mass). 2011;22:582.
26. Pearl J, Paz A. Confounding equivalence in causal inference. J Causal Inference. 2012;2:75–93.
27. Richardson TS, Robins JM. Single world intervention graphs (SWIGs): a unication of the counterfactual and graphical approaches to causality. Working paper number 128. Center for Statistics and the Social Sciences, University of Washington. 2013. http://www.csss.washington.edu/Papers/wp128.pdf.