Targeted maximum likelihood estimation for causal inference in observational studies

American Journal of Epidemiology

Volumn 185, Issue 1, 2017, Pages 65-73

  Schuler, Megan S ^a   Rose, Sherri ^a  

^a HARVARD MEDICAL SCHOOL   (United States)

Author keywords

Causal inference; Machine learning; Observational studies; Super learner; Targeted maximum likelihood estimation

Indexed keywords

EPIDEMIOLOGY; MACHINE LEARNING; MAXIMUM LIKELIHOOD ANALYSIS; OBSERVATIONAL METHOD; OPTIMIZATION; PARAMETER ESTIMATION; PROBABILITY; REGRESSION; TRADE-OFF;

HUMAN; MACHINE LEARNING; MAXIMUM LIKELIHOOD METHOD; OBSERVATIONAL STUDY; SCIENTIST; SIMULATION; STATISTICAL MODEL; CAUSALITY; COMPUTER SIMULATION; DEPRESSION; EPIDEMIOLOGY; EXERCISE; PROCEDURES; PROPENSITY SCORE; PSYCHOLOGY; STANDARDS; STATISTICAL ANALYSIS; STATISTICAL BIAS; STATISTICS AND NUMERICAL DATA;

BIAS (EPIDEMIOLOGY); CAUSALITY; COMPUTER SIMULATION; CONFOUNDING FACTORS (EPIDEMIOLOGY); DATA INTERPRETATION, STATISTICAL; DEPRESSION; EPIDEMIOLOGIC RESEARCH DESIGN; EXERCISE; HUMANS; LIKELIHOOD FUNCTIONS; MACHINE LEARNING; OBSERVATIONAL STUDIES AS TOPIC; PROPENSITY SCORE;

EID: 85014755307     PISSN: 00029262     EISSN: 14766256     Source Type: Journal    
DOI: 10.1093/aje/kww165     Document Type: Article

References (52)

1. Ahern J, Hubbard A, Galea S. Estimating the effects of potential public health interventions on population disease burden: a step-by-step illustration of causal inference methods. Am J Epidemiol. 2009;169(9):1140-1147.
2. Burgess S, Thompson SG. Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects. Am J Epidemiol. 2015;181(4):251-260.
3. Funk MJ, Westreich D, Wiesen C, et al. Doubly robust estimation of causal effects. Am J Epidemiol. 2011;173(7):761-767.
4. Gruber JS, Arnold BF, Reygadas F, et al. Estimation of treatment efficacy with complier average causal effects (CACE) in a randomized stepped wedge trial. Am J Epidemiol. 2014;179(9):1134-1142.
5. Hernan MA, Robins JM. Estimating causal effects from epidemiological data. J Epidemiol Commun Health. 2006;60(7):578-586.
6. Naimi AI, Richardson DB, Cole SR. Causal inference in occupational epidemiology: accounting for the healthy worker effect by using structural nested models. Am J Epidemiol. 2013;178(12):1681-1686.
7. VanderWeele TJ, Vansteelandt S. A weighting approach to causal effects and additive interaction in case-control studies: marginal structural linear odds models. Am J Epidemiol. 2011;174(10):1197-1203.
8. Austin PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behav Res. 2011;46(3):399-424.
9. Austin PC, Stuart EA. Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies. Stat Med. 2015;34(28):3661-3679.
10. Stuart EA. Matching methods for causal inference: a review and a look forward. Stat Sci. 2010;25(1):1-21.
11. Robins J. A new approach to causal inference in mortality studies with a sustained exposure period - application to control of the healthy worker survivor effect. Math Modelling. 1986;7(9-12):1393-1512.
12. Snowden JM, Rose S, Mortimer KM. Implementation of G-computation on a simulated data set: demonstration of a causal inference technique. Am J Epidemiol. 2011;173(7):731-738.
13. Vansteelandt S, Keiding N. Invited commentary: G-computation - lost in translation? Am J Epidemiol. 2011;173(7):739-742.
14. van der Laan MJ, Rose S. Targeted Learning: Causal Inference for Observational and Experimental Data. New York, NY: Springer-Verlag New York; 2011.
15. van der Laan MJ, Rubin DB. Targeted maximum likelihood learning. Int J Biostat. 2006;2(1):Article 11.
16. Rubin DB. Estimating causal effects of treatments in randomized and nonrandomized studies. J Educ Psychol. 1974;66(5):688-701.
17. Holland PW. Statistics and causal inference. J Am Stat Assoc. 1986;81(396):945-960.
18. Imai K, King G, Stuart E. Misunderstandings between experimentalists and observationalists about causal inference. J R Stat Soc Ser A. 2008;171(2):481-502.
19. Imbens GW. Nonparametric estimation of average treatment effects under exogeneity: a review. Rev Econ Stat. 2004;86(1):4-29.
20. Rubin DB. Randomization analysis of experimental data: the fisher randomization test comment. J Am Stat Assoc. 1980;75(371):591-593.
21. Rubin DB. Statistics and causal inference: comment: which ifs have causal answers. J Am Stat Assoc. 1986;81(396):961-962.
22. Greenland S, Robins JM. Identifiability, exchangeability, and epidemiological confounding. Int J Epidemiol. 1986;15(3):413-419.
23. Petersen ML, Porter KE, Gruber S, et al. Diagnosing and responding to violations in the positivity assumption. Stat Methods Med Res. 2012;21(1):31-54.
24. Rothman KJ, Greenland S, Lash TL, eds. Modern Epidemiology. 3rd ed. Philadelphia, PA: Lippincott Williams & Wilkins; 2008.
25. Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika. 1983;70(1):41-55.
26. Lunceford JK, Davidian M. Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study. Stat Med. 2004;23(19):2937-2960.
27. Robins JM, Hernán MA, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology. 2000;11(5):550-560.
28. Kang JDY, Schafer JL. Demystifying double robustness: a comparison of alternative strategies for estimating a population mean from incomplete data. Stat Sci. 2007;22(4):523-539.
29. Robins J, Sued M, Lei-Gomez Q, et al. Comment: performance of double-robust estimators when "inverse probability" weights are highly variable. Stat Sci. 2007;22(4):544-559.
30. van der Laan MJ, Robins JM. Unified Methods for Censored Longitudinal Data and Causality. New York, NY: Springer-Verlag New York; 2002.
31. Radloff LS. The CES-D Scale: a self-report depression scale for research in the general population. Appl Psychol Meas. 1977;1(3):385-401.
32. Gruber S, van der Laan MJ. TMLE: an R package for targeted maximum likelihood estimation. J Stat Softw. 2012;51(13):1-35.
33. Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York, NY: Springer Publishing Company; 2001.
34. Breiman L. Random forests. Mach Learn. 2001;45(1):5-32.
35. Breiman L, Friedman JH, Olshen RA, et al. Classification and Regression Trees. Belmont, CA: Wadsworth International Group; 1984.
36. Dreyfus G. Neural Networks: Methodology and Applications. Berlin, Germany: Springer-Verlag GmbH; 2005.
37. Ridgeway G. Looking for lumps: boosting and bagging for density estimation. Comput Stat Data Anal. 2002;38(4):379-392.
38. Cleveland WS, Devlin SJ. Locally weighted regression: an approach to regression analysis by local fitting. J Am Stat Assoc. 1988;83(403):596-610.
39. Rose S. Mortality risk score prediction in an elderly population using machine learning. Am J Epidemiol. 2013;177(5):443-452.
40. Kreif N, Gruber S, Radice R, et al. Evaluating treatment effectiveness under model misspecification: a comparison of targeted maximum likelihood estimation with bias-corrected matching. Stat Methods Med Res. 2016;25(5):2315-2336.
41. Pirracchio R, Petersen ML, van der Laan M. Improving propensity score estimators' robustness to model misspecification using Super Learner. Am J Epidemiol. 2015;181(2):108-119.
42. Dudoit S, van der Laan MJ. Asymptotics of cross-validated risk estimation in estimator selection and performance assessment. Stat Methodol. 2005;2(2):131-154.
43. van der Laan MJ, Polley EC, Hubbard AE. Super learner. Stat Appl Genet Molec Biol. 2007;6:Article 25.
44. Westreich D, Lessler J, Funk MJ. Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression. J Clin Epidemiol. 2010;63(8):826-833.
45. Polley EC, van der Laan MJ. SuperLearner: Super Learner Prediction. (Package version 2.0-15). http://cran.rproject.org/web/packages/SuperLearner/index.html. Published July 16, 2014. Accessed July 1, 2015.
46. Rubin DB. On principles for modeling propensity scores in medical research. Pharmacoepidem Drug Saf. 2004;13(12):855-857.
47. McCaffrey DF, Griffin BA, Almirall D, et al. A tutorial on propensity score estimation for multiple treatments using generalized boosted models. Stat Med. 2013;32(19):3388-3414.
48. McCaffrey DF, Ridgeway G, Morral AR. Propensity score estimation with boosted regression for evaluating causal effects in observational studies. Psychol Methods. 2004;9(4):403-425.
49. Lee BK, Lessler J, Stuart EA. Weight trimming and propensity score weighting. PLoS One. 2011;6(3):e18174.
50. Setoguchi S, Schneeweiss S, Brookhart MA, et al. Evaluating uses of data mining techniques in propensity score estimation: a simulation study. Pharmacoepidem Drug Saf. 2008;17(6):546-555.
51. Hill J, Weiss C, Zhai F. Challenges with propensity score strategies in a high-dimensional setting and a potential alternative. Multivariate Behav Res. 2011;46(3):477-513.
52. Luellen JK, Shadish WR, Clark MH. Propensity scores: an introduction and experimental test. Eval Rev. 2005;29(6):530-558.