References

Alam, Shomoita, Erica EM Moodie, and David A Stephens. 2019. “Should a Propensity Score Model Be Super? The Utility of Ensemble Procedures for Causal Adjustment.” Statistics in Medicine 38 (9): 1690–1702.

Austin, Peter C. 2011. “A Tutorial and Case Study in Propensity Score Analysis: An Application to Estimating the Effect of in-Hospital Smoking Cessation Counseling on Mortality.” Multivariate Behavioral Research 46 (1): 119–51.

Austin, Peter C, and Elizabeth A Stuart. 2015. “Moving Towards Best Practice When Using Inverse Probability of Treatment Weighting (IPTW) Using the Propensity Score to Estimate Causal Treatment Effects in Observational Studies.” Statistics in Medicine 34 (28): 3661–79.

Balzer, Laura B, and Ted Westling. 2021. “Demystifying Statistical Inference When Using Machine Learning in Causal Research.” American Journal of Epidemiology.

Connors, Alfred F, Theodore Speroff, Neal V Dawson, Charles Thomas, Frank E Harrell, Douglas Wagner, Norman Desbiens, et al. 1996. “The Effectiveness of Right Heart Catheterization in the Initial Care of Critically III Patients.” Jama 276 (11): 889–97. https://tinyurl.com/Connors1996.

Coyle, Jeremy R, Nima S Hejazi, Ivana Malenica, and Oleg Sofrygin. 2021. sl3: Modern Pipelines for Machine Learning and Super Learning. https://github.com/tlverse/sl3. https://doi.org/10.5281/zenodo.1342293.

Coyle, Jeremy R, Nima S Hejazi, Ivana Melencia, Rachael Phillips, and Alex Hubbard. 2021. Targeted Learning in R: Causal Data Science with the tlverse Software Ecosystem. https://github.com/tlverse/tlverse-handbook. https://tlverse.org/tlverse-handbook/.

Gruber, S., M. Van Der Laan, and C. Kennedy. 2020. Package ’Tmle’. https://cran.r-project.org/web/packages/tmle/tmle.pdf.

Gruber, Susan, and Mark J van der Laan. 2010. “A Targeted Maximum Likelihood Estimator of a Causal Effect on a Bounded Continuous Outcome.” The International Journal of Biostatistics 6 (1).

Gruber, Susan, and Mark J Van Der Laan. 2009. “Targeted Maximum Likelihood Estimation: A Gentle Introduction.”

Keele, Luke, and Dylan S Small. 2018. “Pre-Analysis Plan for a Comparison of Matching and Black Box-Based Covariate Adjustment.” Observational Studies 4 (1): 97–110.

———. 2021. “Comparing Covariate Prioritization via Matching to Machine Learning Methods for Causal Inference Using Five Empirical Applications.” The American Statistician, 1–9.

Laan, Mark J van der, and Maya L Petersen. 2012. Targeted Learning. NY: Springer.

Luque-Fernandez, Miguel Angel, Michael Schomaker, Bernard Rachet, and Mireille E Schnitzer. 2018. “Targeted Maximum Likelihood Estimation for a Binary Treatment: A Tutorial.” Statistics in Medicine 37 (16): 2530–46.

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

Naimi, Ashley I, Stephen R Cole, and Edward H Kennedy. 2017. “An Introduction to g Methods.” International Journal of Epidemiology 46 (2): 756–62.

Naimi, Ashley I, Alan E Mishler, and Edward H Kennedy. 2017. “Challenges in Obtaining Valid Causal Effect Estimates with Machine Learning Algorithms.” arXiv Preprint arXiv:1711.07137.

Nguyen, Tri-Long, Gary S Collins, Jessica Spence, Jean-Pierre Daurès, PJ Devereaux, Paul Landais, and Yannick Le Manach. 2017. “Double-Adjustment in Propensity Score Matching Analysis: Choosing a Threshold for Considering Residual Imbalance.” BMC Medical Research Methodology 17 (1): 1–8.

Rose, Sherri. 2013. “Mortality Risk Score Prediction in an Elderly Population Using Machine Learning.” American Journal of Epidemiology 177 (5): 443–52.

———. 2020. “Intersections of Machine Learning and Epidemiological Methods for Health Services Research.” International Journal of Epidemiology 49 (6): 1763–70.

Schuler, Megan S, and Sherri Rose. 2017. “Targeted Maximum Likelihood Estimation for Causal Inference in Observational Studies.” American Journal of Epidemiology 185 (1): 65–73.

Snowden, Jonathan M, Sherri Rose, and Kathleen M Mortimer. 2011. “Implementation of g-Computation on a Simulated Data Set: Demonstration of a Causal Inference Technique.” American Journal of Epidemiology 173 (7): 731–38.