\(\DeclareMathOperator{\expit}{expit}\) \(\DeclareMathOperator{\logit}{logit}\) \(\DeclareMathOperator*{\argmin}{\arg\!\min}\) \(\newcommand{\indep}{\perp\!\!\!\perp}\) \(\newcommand{\coloneqq}{\mathrel{=}}\) \(\newcommand{\R}{\mathbb{R}}\) \(\newcommand{\E}{\mathbb{E}}\) \(\newcommand{\M}{\mathcal{M}}\) \(\renewcommand{\P}{\mathbb{P}}\) \(\newcommand{\I}{\mathbb{I}}\) \(\newcommand{\1}{\mathbbm{1}}\)
Skip to main content

[ACIC 2022] Targeted Learning in the tlverse: Causal Inference Meets Machine Learning

References

Baker, Monya. 2016. “Is There a Reproducibility Crisis? A Nature Survey Lifts the Lid on How Researchers View the Crisis Rocking Science and What They Think Will Help.” Nature 533 (7604): 452–55.

Benkeser, David, and Mark J van der Laan. 2016. “The Highly Adaptive Lasso Estimator.” In 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA). IEEE. https://doi.org/10.1109/dsaa.2016.93.

Breiman, Leo. 1996. “Stacked Regressions.” Machine Learning 24 (1): 49–64.

Buckheit, Jonathan B, and David L Donoho. 1995. “Wavelab and Reproducible Research.” In Wavelets and Statistics, 55–81. Springer.

Coyle, Jeremy R, Nima S Hejazi, and Mark J van der Laan. 2020. hal9001: The Scalable Highly Adaptive Lasso. https://doi.org/10.5281/zenodo.3558313.

Dı́az, Iván, and Mark J van der Laan. 2011. “Super Learner Based Conditional Density Estimation with Application to Marginal Structural Models.” The International Journal of Biostatistics 7 (1): 1–20.

———. 2012. “Population Intervention Causal Effects Based on Stochastic Interventions.” Biometrics 68 (2): 541–49.

———. 2018. “Stochastic Treatment Regimes.” In Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies, 167–80. Springer Science & Business Media.

Dudoit, Sandrine, and Mark J van der Laan. 2005. “Asymptotics of Cross-Validated Risk Estimation in Estimator Selection and Performance Assessment.” Statistical Methodology 2 (2): 131–54.

Haneuse, Sebastian, and Andrea Rotnitzky. 2013. “Estimation of the Effect of Interventions That Modify the Received Treatment.” Statistics in Medicine 32 (30): 5260–77.

Hejazi, Nima S, David C Benkeser, and Mark J van der Laan. 2020. haldensify: Highly Adaptive Lasso Conditional Density Estimation. https://github.com/nhejazi/haldensify. https://doi.org/10.5281/zenodo.3698329.

Hejazi, Nima S, Jeremy R Coyle, and Mark J van der Laan. 2020. “hal9001: Scalable Highly Adaptive Lasso Regression in R.” Journal of Open Source Software. https://doi.org/10.21105/joss.02526.

Luby, Stephen P, Mahbubur Rahman, Benjamin F Arnold, Leanne Unicomb, Sania Ashraf, Peter J Winch, Christine P Stewart, et al. 2018. “Effects of Water Quality, Sanitation, Handwashing, and Nutritional Interventions on Diarrhoea and Child Growth in Rural Bangladesh: A Cluster Randomised Controlled Trial.” The Lancet Global Health 6 (3): e302–e315.

Munafò, Marcus R, Brian A Nosek, Dorothy VM Bishop, Katherine S Button, Christopher D Chambers, Nathalie Percie Du Sert, Uri Simonsohn, Eric-Jan Wagenmakers, Jennifer J Ware, and John PA Ioannidis. 2017. “A Manifesto for Reproducible Science.” Nature Human Behaviour 1 (1): 0021.

Naimi, Ashley I, and Laura B Balzer. 2018. “Stacked Generalization: An Introduction to Super Learning.” European Journal of Epidemiology 33 (5): 459–64.

Nature Editorial. 2015a. “How Scientists Fool Themselves — and How They Can Stop.” Nature 526 (7572).

———. 2015b. “Let’s Think About Cognitive Bias.” Nature 526 (7572).

Nosek, Brian A, Charles R Ebersole, Alexander C DeHaven, and David T Mellor. 2018. “The Preregistration Revolution.” Proceedings of the National Academy of Sciences 115 (11): 2600–2606.

Pearl, Judea. 2009. Causality: Models, Reasoning, and Inference. Cambridge University Press.

Peng, Roger. 2015. “The Reproducibility Crisis in Science: A Statistical Counterattack.” Significance 12 (3): 30–32.

Phillips, Rachael V, Mark J van der Laan, Hana Lee, and Susan Gruber. 2022. “Practical Considerations for Specifying a Super Learner.” https://doi.org/10.48550/ARXIV.2204.06139.

Polley, Eric C, and Mark J van der Laan. 2010. “Super Learner in Prediction.” Bepress.

Pullenayegum, Eleanor M, Robert W Platt, Melanie Barwick, Brian M Feldman, Martin Offringa, and Lehana Thabane. 2016. “Knowledge Translation in Biostatistics: A Survey of Current Practices, Preferences, and Barriers to the Dissemination and Uptake of New Statistical Methods.” Statistics in Medicine 35 (6): 805–18.

Stark, Philip B, and Andrea Saltelli. 2018. “Cargo-Cult Statistics and Scientific Crisis.” Significance 15 (4): 40–43.

Stromberg, Arnold, and others. 2004. “Why Write Statistical Software? The Case of Robust Statistical Methods.” Journal of Statistical Software 10 (5): 1–8.

Szucs, Denes, and John Ioannidis. 2017. “When Null Hypothesis Significance Testing Is Unsuitable for Research: A Reassessment.” Frontiers in Human Neuroscience 11: 390.

van der Laan, Mark J, Eric C Polley, and Alan E Hubbard. 2007. “Super Learner.” Statistical Applications in Genetics and Molecular Biology 6 (1).

van der Laan, Mark J, and Sherri Rose. 2011. Targeted Learning: Causal Inference for Observational and Experimental Data. Springer Science & Business Media.

van der Laan, Mark J, and Richard JCM Starmans. 2014. “Entering the Era of Data Science: Targeted Learning and the Integration of Statistics and Computational Data Analysis.” Advances in Statistics 2014.

Wickham, Hadley. 2014. Advanced R. Chapman; Hall/CRC.

Young, Jessica G, Miguel A Hernán, and James M Robins. 2014. “Identification, Estimation and Approximation of Risk Under Interventions That Depend on the Natural Value of Treatment Using Observational Data.” Epidemiologic Methods 3 (1): 1–19.