Principles of confounder selection

European Journal of Epidemiology

Volumn 34, Issue 3, 2019, Pages 211-219

  VanderWeele, Tyler J ^a  

^a HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

Causal inference; Collider; Confounder; Covariate adjustment; Selection

Indexed keywords

ARTICLE; INSTRUMENTAL VARIABLE ANALYSIS; EPIDEMIOLOGY; HUMAN; METHODOLOGY;

CONFOUNDING FACTORS (EPIDEMIOLOGY); HUMANS; RESEARCH DESIGN;

EID: 85062657470     PISSN: 03932990     EISSN: 15737284     Source Type: Journal    
DOI: 10.1007/s10654-019-00494-6     Document Type: Article

References (57)

1. Pearl J. Causal diagrams for empirical research (with discussion). Biometrika. 1995;82:669–710.
2. Pearl J. Causality: models, reasoning, and inference. 2nd ed. Cambridge: Cambridge University Press; 2009.
3. Huang Y, Valtorta M. Pearl’s calculus of interventions is complete. In: Twenty second conference on uncertainty in artificial intelligence.
4. Shpitser I, VanderWeele TJ, Robins JM. On the validity of covariate adjustment for estimating causal effects. In: Proceedings of the 26th conference on uncertainty and artificial intelligence. Corvallis: AUAI Press; (2010), p. 527–536.
5. Greenland S. Quantifying biases in causal models: classical confounding vs collider-stratification bias. Epidemiology. 2003;14:300–6.
6. Ding P, Miratrix LW. To adjust or not to adjust? Sensitivity analysis of M-bias and butterfly-bias (with comments). J Causal Infer. 2015;3:41–57.
7. Sjølander A. Letter to the editor. Stat Med. 2009;28:1416–20.
8. Cole SR, Platt RW, Schisterman EF, Chu H, Westreich D, Richardson D, Poole C. Illustrating bias due to conditioning on a collider. Int J Epidemiol. 2010;39:417–20.
9. VanderWeele TJ, Shpitser I. A new criterion for confounder selection. Biometrics. 2011;67:1406–13.
10. VanderWeele TJ. Explanation in causal inference: methods for mediation and interaction. New York: Oxford University Press; 2015.
11. Bhattacharya J, Vogt W. Do instrumental variables belong in propensity scores? Int J Stat Econ. 2012;9:107–27.
12. Myers JA, Rassen JA, Gagne JJ, Huybrechts KF, Schneeweiss S, Rothman KJ, Joffee MM, Glynn RJ. Effects of adjusting for instrumental variables on bias and precision of effect estimates. Am J Epidemiol. 2011;174:1213–22.
13. Pearl J. On a class of bias-amplifying variables that endanger effect estimates. In: Grunwald P, Spirtes P, editors. Proceedings of the 26th conferenec on uncertainty in artificial intelligence (UAI 2010). Corvallis, Oregon: Association for Uncetainty in Artificial Intelligence; 2010. p. 425–32.
14. Middleton JA, Scott MA, Diakow R, Hill JL. Bias amplification and bias unmasking. Polit Anal. 2016;24:307–23.
15. Wooldridge J. Should instrumental variables be used as matching variables? Res Econ. 2016;70:232–7.
16. Ding P, VanderWeele TJ, Robins JM. Instrumental variables as bias amplifiers with general outcome and confounding. Biometrika. 2017;104:291–302.
17. Ogburn EL, VanderWeele TJ. Bias attenuation results for nondifferentially mismeasured ordinal and coarsened confounders. Biometrika. 2013;100:241–8.
18. Ogburn EL, VanderWeele TJ. On the nondifferential misclassification of a binary confounder. Epidemiology. 2012;23:433–9.
19. Greenland S, Robins JM. Identifiability, exchangeability, and epidemiologic confounding. Int J Epidemiol. 1986;15:413–9.
20. Robins JM. Estimation of the time-dependent accelerated failure time model in the presence of confounding factors. Biometrika. 1992;79:321–34.
21. Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika. 1983;1983(70):41–55.
22. Barnow BS, Cain GG, Goldberger AS. Issues in the analysis of selectivity bias. In: Stromsdorfer E, Farkas G, editors. Evaluation studies, vol. 5. San Francisco: Sage; 1980.
23. Imbens GW. Nonparametric estimation of average treatment effects under exogeneity: a review. Rev Econ Stat. 2004;86:4–29.
24. VanderWeele TJ. Concerning the consistency assumption in causal inference. Epidemiology. 2009;20:880–3.
25. Rubin DB. Author’s reply (to Judea Pearl’s and Arvid Sjolander’s letters to the editor). Stat Med. 2009;28:1420–3.
26. Rubin DB. For objective causal inference, design trumps analysis. Ann Appl Stat. 2008;3:808–40.
27. Weinberg CR. Toward a clearer definition of confounding. Am J Epidemiol. 1993;137:1–8.
28. Glymour MM, Weuve J, Chen JT. Methodological challenges in causal research on racial and ethnic patterns of cognitive trajectories: measurement, selection, and bias. Neuropsychol Rev. 2008;18:194–213.
29. Greenland S. An introduction to instrumental variables for epidemiologists. Int J Epidemiol. 2000;29:722–9.
30. Angrist JD, Imbens GW, Rubin DB. Identification of causal effects using instrumental variables (with discussion). J Am Stat Assoc. 1996;91:444–72.
31. Hernán MA, Robins JM. Instruments for causal inference: an epidemiologist’s dream? Epidemiology. 2006;17:360–72.
32. Greenland S. The effect of misclassification in the presence of covariates. Am J Epidemiol. 1980;112:564–9.
33. Brenner H. Bias due to non-differential misclassification of polytomous confounders. J Clin Epidemiol. 1993;46:57–63.
34. Schlesselman JJ. Assessing effects of confounding variables. Am J Epidemiol. 1978;108:3–8.
35. Rosenbaum PR, Rubin DB. Assessing sensitivity to an unobserved binary covariate in an observational study with binary outcome. J R Stat Soc Ser B. 1983;45:212–8.
36. Flanders WD, Khoury MJ. Indirect assessment of confounding: graphic description and limits on effect of adjusting for covariates. Epidemiology. 1990;1:239–46.
37. Lash TL, Fox MP, Fink AK. Applying quantitative bias analysis to epidemiologic data. New York: Springer; 2009.
38. Ding P, VanderWeele TJ. Sensitivity analysis without assumptions. Epidemiology. 2016;27(3):368–77.
39. VanderWeele TJ, Ding P. Sensitivity analysis in observational research: introducing the E-value. Ann Intern Med. 2017;167:268–74.
40. Hernán MA, Robins JM. Causal inference. Boca Raton: Chapman & Hall/CRC; 2018.
41. Robins JM. Causal inference from complex longitudinal data. In: Berkane M, editor. Latent variable modeling and applications to causality (Los Angeles, CA, 1994). Lecture notes in statistics, vol. 120. New York: Springer; 1997. p. 69–117.
42. Taubman SL, Robins JM, Mittleman MA, Hernán MA. Intervening on risk factors for coronary heart disease: an application of the parametric g-formula. Int J Epidemiol. 2009;38(6):1599–611.
43. Garcia-Aymerich J, Varraso R, Danaei G, Camargo CA, Hernán MA. Incidence of adult-onset asthma after hypothetical interventions on body mass index and physical activity. An application of the parametric g-formula. Am J Epidemiol. 2014;179(1):20–6.
44. Danaei G, Pan A, Hu FB, Hernán MA. Hypothetical lifestyle interventions in middle-aged women and risk of type 2 diabetes: a 24-year prospective study. Epidemiology. 2013;24(1):122–8.
45. Lajous M, Willett WC, Robins JM, Young JG, Rimm EB, Mozaffarian D, Hernán MA. Changes in fish consumption in midlife and the risk of coronary heart disease in men and women. Am J Epidemiol. 2013;178(3):382–91.
46. VanderWeele TJ, Jackson JW, Li S. Causal inference and longitudinal data: a case study of religion and mental health. Soc Psychiatry Psychiatr Epidemiol. 2016;51:1457–66.
47. Maldonado G, Greenland S. Simulation study of confounder-selection strategies. Am J Epidemiol. 1993;138:923–36.
48. Greenland S. Invited commentary: variable selection versus shrinkage in the control of multiple confounders. Am J Epidemiol. 2008;167:523–9.
49. Belloni A, Chernozhukov V, Hansen C. Inference on treatment effects after selection among high-dimensional controls. Rev Econ Stud. 2014;81:608–50.
50. Chernozhukov V, Hansen C, Spindler M. Valid post-selection and post-regularization inference: an elementary, general approach. Annu Rev Econ. 2015;7:649–88.
51. Lee JD, Sun DL, Sun Y, Taylor JE. Exact post-selection inference with the lasso. Ann Stat. 2016;44(3):907–27.
52. Greenland S, Robins JM, Pearl J. Confounding and collapsibility in causal inference. Stat Sci. 1999;14:29–46.
53. Schneeweiss S, Rassen JA, Glynn RJ, Avorn J, Mogun H, Brookhart MA. High-dimensional propensity score adjustment in studies of treatment effects using health care claims data. Epidemiology. 2009;20(4):512–22.
54. Rassen JA, Glynn RJ, Brookhart MA, Schneeweiss S. Covariate selection in high-dimensional propensity score analyses of treatment effects in small samples. Am J Epidemiol. 2011;173(12):1404–13.
55. van der Laan JM, Rose S. Targeted learning in data science: causal inference for complex longitudinal studies. New York: Springer; 2018.
56. van der Laan JM, Rose S. Targeted learning: causal inference for observational and experimental data. New York: Springer; 2011.
57. Schuler M, Rose S. Targeted maximum likelihood estimation for causal inference in observational studies. Am J Epidemiol. 2017;185(1):65–73.