References

Chernozhukov, Victor, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, and James Robins. 2018. “Double/Debiased Machine Learning for Treatment and Structural Parameters.” The Econometrics Journal 21 (1). https://doi.org/10.1111/ectj.12097.

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.

Dı́az, Iván, and Nima S Hejazi. 2020. “Causal Mediation Analysis for Stochastic Interventions.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 82 (3): 661–83.

Dı́az, Iván, Nima S Hejazi, Kara E Rudolph, and Mark J van der Laan. 2020. “Non-Parametric Efficient Causal Mediation with Intermediate Confounders.” Biometrika. https://doi.org/10.1093/biomet/asaa085.

Hejazi, Nima S, and David C Benkeser. 2020a. txshift: Efficient Estimation of the Causal Effects of Stochastic Interventions. https://doi.org/10.5281/zenodo.4070042.

———. 2020b. “txshift: Efficient Estimation of the Causal Effects of Stochastic Interventions in R.” Journal of Open Source Software. https://doi.org/10.21105/joss.02447.

Hejazi, Nima S, and Iván Dı́az. 2020. medshift: Causal Mediation Analysis for Stochastic Interventions. https://github.com/nhejazi/medshift.

Hejazi, Nima S, Iván Dı́az, and Kara E Rudolph. 2021. medoutcon: Efficient Causal Mediation Analysis Under Intermediate Confounding. https://github.com/nhejazi/medoutcon.

Hejazi, Nima S, Kara E Rudolph, Mark J van der Laan, and Iván Dı́az. 2020. “Nonparametric Causal Mediation Analysis for Stochastic Interventional (in) Direct Effects.” arXiv Preprint arXiv:2009.06203.

Kennedy, Edward H. 2018. “Nonparametric Causal Effects Based on Incremental Propensity Score Interventions.” Journal of the American Statistical Association, nos. just-accepted.

Klaassen, Chris AJ. 1987. “Consistent Estimation of the Influence Function of Locally Asymptotically Linear Estimators.” The Annals of Statistics, 1548–62.

Rudolph, Kara, Ivan Diaz, Nima Hejazi, Mark van der Laan, Sean Luo, Matisyahu Shulman, Aimee Campbell, John Rotrosen, and Edward Nunes. 2020. “Explaining Differential Effects of Medication for Opioid Use Disorder Using a Novel Approach Incorporating Mediating Variables.” Addiction.

Rudolph, Kara E, Dana E Goin, Diana Paksarian, Rebecca Crowder, Kathleen R Merikangas, and Elizabeth A Stuart. 2019. “Causal Mediation Analysis with Observational Data: Considerations and Illustration Examining Mechanisms Linking Neighborhood Poverty to Adolescent Substance Use.” American Journal of Epidemiology 188 (3): 598–608.

van der Laan, Mark J, Jeremy R Coyle, Nima S Hejazi, Ivana Malenica, Rachael V Phillips, and Alan E Hubbard. 2022. Targeted Learning in R: Causal Data Science with the tlverse Software Ecosystem. CRC Press. https://tlverse.org/tlverse-handbook.

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

Zheng, Wenjing, and Mark J van der Laan. 2011. “Cross-Validated Targeted Minimum-Loss-Based Estimation.” In Targeted Learning, 459–74. Springer.