Invited commentary: Understanding bias amplification

American Journal of Epidemiology

Volumn 174, Issue 11, 2011, Pages 1223-1227

  Pearl, Judea ^a  

^a University of California   (United States)

Author keywords

Bias (epidemiology); Confounding factors (epidemiology); Epidemiologic methods; Instrumental variable; Precision; Simulation; Variable selection

Indexed keywords

ASSESSMENT METHOD; COVARIANCE ANALYSIS; EPIDEMIOLOGY; ERROR ANALYSIS; NUMERICAL MODEL; PRECISION;

ACCURACY; AWARENESS; CONFOUNDING VARIABLE; EPIDEMIOLOGY; EXPOSURE; HEALTH SCIENCE; HIP FRACTURE; INSTRUMENTAL VARIABLE ANALYSIS; MATHEMATICAL ANALYSIS; MULTIVARIATE ANALYSIS; NONLINEAR SYSTEM; PROPENSITY SCORE; REVIEW; SCIENTIST; SYSTEMATIC ERROR; NOTE;

BIAS (EPIDEMIOLOGY); EPIDEMIOLOGIC METHODS;

EID: 84856039288     PISSN: 00029262     EISSN: 14766256     Source Type: Journal    
DOI: 10.1093/aje/kwr352     Document Type: Review

References (17)

1. Bhattacharya J, Vogt W. Do Instrumental Variables Belong in Propensity Scores? (NBER Technical Working Paper no. 343). Cambridge, MA: National Bureau of Economic Research; 2007.
2. Wooldridge J. Should instrumental variables be used as matching variables? East Lansing, MI: Michigan State University; 2009. (https://www.msu.edu/~ec/faculty/wooldridge/current%20research/treat1r6.pdf). (Accessed July 2010).
3. Pearl J. On a class of bias-amplifying variables that endanger effect estimates. In: Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence (UAI 2010). Corvallis, OR: Association for Uncertainty in Artificial Intelligence; 2010:425-432. (http://ftp.cs.ucla.edu/pub/stat-ser/ r356.pdf). (Accessed September 2011).
4. Myers JA, Rassen JA, Gagne JJ, et al. Effects of adjusting for instrumental variables on bias and precision of effect estimates. Am J Epidemiol. 2011;174(11):1213-1222.
5. White H, Lu X. Causal diagrams for treatment effect estimation with application to efficient covariate selection. Rev Econ Stat. In press.
6. Patrick AR, Schneeweiss S, Brookhart MA, et al. The implications of propensity score variable selection strategies in pharmacoepidemiology: an empirical illustration. Pharmacoepidemiol Drug Saf. 2011;20(6):551-559.
7. Hirano K, Imbens G. Estimation of causal effects using propensity score weighting: an application to data on right heart catheterization. Health Serv Outcome Res Methodol. 2001;2(3-4):259-278.
8. Rubin D. Author's reply: Should observational studies be designed to allow lack of balance in covariate distributions across treatment groups? [letter]. Stat Med. 2009;28(9):1420-1423.
9. Pearl J. Myth, Confusion, and Science in Causal Analysis. (Technical report R-348). Los Angeles, CA: Department of Computer Science, University of California, Los Angeles; 2009. (http://ftp.cs.ucla.edu/pub/stat-ser/r348.pdf). (Accessed May 2009).
10. Day NE, Byar DP, Green SB. Overadjustment in case-control studies. Am J Epidemiol. 1980;112(5):696-706. (Pubitemid 11130683)
11. Thomas DC, Greenland S. The relative efficiencies of matched and independent sample designs for case-control studies. J Chronic Dis. 1983;36(10):685-697. (Pubitemid 14246890)
12. Hill J. Discussion of research using propensity-score matching: comments on 'A critical appraisal of propensity-score matching in the medical literature between 1996 and 2003' by Peter Austin, Statistics in Medicine. Stat Med. 2008;27(12):2055-2061. (Pubitemid 351772302)
13. Austin PC. A critical appraisal of propensity-score matching in the medical literature between 1996 and 2003. Stat Med. 2008;27(12):2037-2049. (Pubitemid 351772300)
14. Morgan SL, Winship C. Counterfactuals and Causal Inference: Methods and Principles for Social Research. New York, NY: Cambridge University Press; 2007:83.
15. Pearl J, Paz A. Confounding equivalence in causal inference. In: Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence (UAI 2010). Corvallis, OR: Association for Uncertainty in Artificial Intelligence; 2010:433-441.
16. Hahn J. Functional restriction and efficiency in causal inference. Rev Econ Stat. 2004;86(1):73-76. (Pubitemid 38422767)
17. Bareinboim E, Pearl J. Controlling Selection Bias in Causal Inference. Los Angeles, CA: Department of Computer Science, University of California, Los Angeles; 2011. (http://ftp.cs.ucla.edu/pub/stat-ser/r381.pdf). (Accessed September 2011).