References
Wolpert DH. Stacked generalization. Neural Netw. 1992;5(2):241–59.
Naimi AI, Balzer LB. Stacked generalization: an introduction to super learning. Eur J Epidemiol. 2018. https://doi.org/10.1007/s10654-018-0390-z.
Murphy K. Machine learning: a probabilistic perspective. 1st ed. Cambridge: The MIT press; 2012.
Greenland S. Model-based estimation of relative risks and other epidemiologic measures in studies of common outcomes and in case-control studies. Am J Epidemiol. 2004;160(4):301–5. https://doi.org/10.1093/aje/kwh221.
Greenland S. Multivariate estimation of exposure-specific incidence from case-control studies. J Chronic Dis. 1981;34(9–10):445–53.
Muller CJ, MacLehose RF. Estimating predicted probabilities from logistic regression: different methods correspond to different target populations. Int J Epidemiol. 2014;43(3):962–70. https://doi.org/10.1093/ije/dyu029.
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):826–33.
van der Laan MJ, Rose S. Targeted learning: causal inference for observational and experimental data. Berlin: Springer; 2011.
van der Laan MJ, Starmans RJ. Entering the era of data science: targeted learning and the integration of statistics and computational data analysis. Advances in Statistics; 2014.
Chernozhukov V, Chetverikov D, Demirer M, et al. Double/debiased machine learning for treatment and structural parameters. Econom J. 2017;21:C1–C68.
Lee BK, Lessler J, Stuart EA. Improving propensity score weighting using machine learning. Stat Med. 2010;29(3):337–46.
Watkins S, Jonsson-Funk M, Brookhart MA, Rosenberg SA, O’Shea TM, Daniels J. An empirical comparison of tree-based methods for propensity score estimation. Health Serv Res. 2013;48(5):1798–817. https://doi.org/10.1111/1475-6773.12068.
Gelman A, Loken E. The garden of forking paths: why multiple comparisons can be a problem, even when there is no “fishing expedition” or “p-hacking” and the research hypothesis was posited ahead of time. Department of Statistics, Columbia University. 2013.
Polley EC, van der Laan MJ. Super learner in prediction. Division of Biostatistics, University of California, Berkeley2010 Contract No.: 222.
Robins JM. Data, design, and background knowledge in etiologic inference. Epidemiology. 2001;12(3):313–20.
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This work was supported by the NIH/NICHD (Grant # DP2-HD08-4070) and NIH/NIAID (Grant # K01AI125087).
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Keil, A.P., Edwards, J.K. You are smarter than you think: (super) machine learning in context. Eur J Epidemiol 33, 437–440 (2018). https://doi.org/10.1007/s10654-018-0405-9
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DOI: https://doi.org/10.1007/s10654-018-0405-9