References
Avin, Chen, Ilya Shpitser, and Judea Pearl. 2005. “Identifiability
of Path-Specific Effects.” In IJCAI International Joint
Conference on Artificial Intelligence, 357–63.
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.
Bembom, Oliver, and Mark J van der Laan. 2007. “A Practical
Illustration of the Importance of Realistic Individualized Treatment
Rules in Causal Inference.” Electronic Journal of
Statistics 1: 574–96.
Bengtsson, Henrik. 2021. “A Unifying Framework for Parallel and
Distributed Processing in r Using Futures.” The R
Journal. https://doi.org/10.32614/RJ-2021-048.
Benkeser, David, and Jialu Ran. 2021. “Nonparametric Inference for
Interventional Effects with Multiple Mediators.” Journal of
Causal Inference. https://doi.org/10.1515/jci-2020-0018.
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.
———. 2001. “Random Forests.” Machine Learning 45
(1): 5–32.
Buckheit, Jonathan B, and David L Donoho. 1995. “Wavelab and
Reproducible Research.” In Wavelets and Statistics,
55–81. Springer.
Chakraborty, Bibhas, and Erica EM Moodie. 2013. Statistical Methods
for Dynamic Treatment Regimes: Reinforcement Learning, Causal Inference,
and Personalized Medicine (Statistics for Biology and Health).
Springer.
Coyle, Jeremy R, and Nima S Hejazi. 2018. “Origami: A Generalized
Framework for Cross-Validation in r.” Journal of Open Source
Software 3 (21). https://doi.org/10.21105/joss.00512.
Coyle, Jeremy R, Nima S Hejazi, Ivana Malenica, and Rachael V Phillips.
n.d.
origami: Generalized Framework for
Cross-Validation. https://doi.org/10.5281/zenodo.835602.
Coyle, Jeremy R, Nima S Hejazi, Ivana Malenica, Rachael V Phillips,
Benjamin F Arnold, Andrew Mertens, Jade Benjamin-Chung, et al. 2021.
“Targeting Learning: Robust Statistics for
Reproducible Research.” arXiv. https://arxiv.org/abs/2006.07333.
Coyle, Jeremy R, Nima S Hejazi, Rachael V Phillips, Lars WP van der
Laan, and Mark J van der Laan. 2022. hal9001: The Scalable Highly Adaptive Lasso.
https://doi.org/10.5281/zenodo.3558313.
Davison, Anthony Christopher, and David Victor Hinkley. 1997.
Bootstrap Methods and Their Application. Cambridge University
Press.
Dawid, A Philip. 2000. “Causal Inference Without
Counterfactuals.” Journal of the American Statistical
Association 95 (450): 407–24.
Didelez, Vanessa, Philip Dawid, and Sara Geneletti. 2006. “Direct
and Indirect Effects of Sequential Treatments.” In
Proceedings of the 22nd Annual Conference on Uncertainty in
Artificial Intelligence, 138–46.
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. https://doi.org/10.1111/rssb.12362.
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.
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.
———. 2013. “Sensitivity Analysis for Causal Inference Under
Unmeasured Confounding and Measurement Error Problems.” The
International Journal of Biostatistics 9 (2): 149–60. https://doi.org/10.1515/ijb-2013-0004.
———. 2018. “Stochastic Treatment Regimes.” In Targeted
Learning in Data Science: Causal Inference for Complex Longitudinal
Studies, 167–80. Springer Science & Business Media.
Donoho, David. 2017. “50 Years of Data Science.”
Journal of Computational and Graphical Statistics 26 (4):
745–66.
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.
Fisher, Ronald Aylmer. 1946. Statistical Methods for Research
Workers. 10th ed. Oliver; Boyd.
Gruber, Susan, Rachael V Phillips, Hana Lee, John Concato, and Mark van
der Laan. 2022. “Evaluating and Improving Real-World Evidence with
Targeted Learning.” arXiv Preprint arXiv:2208.07283.
Gruber, Susan, Rachael V Phillips, Hana Lee, Martin Ho, John Concato,
and Mark J van der Laan. 2023. “Targeted Learning:
Toward a Future Informed by Real-World Evidence.” Statistics
in Biopharmaceutical Research. https://doi.org/10.1080/19466315.2023.2182356.
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. 2021. “Semiparametric Statistical Methods for
Causal Inference with Stochastic Treatment Regimes.” PhD thesis,
University of California, Berkeley. https://www.stat.berkeley.edu/~nhejazi/publications/thesis-phd-biostat.pdf.
Hejazi, Nima S, David C Benkeser, and Mark J van der Laan. 2022.
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.
Hejazi, Nima S, Kara E Rudolph, Mark J van der Laan, and Iván Dı́az.
2022. “Nonparametric Causal Mediation Analysis for Stochastic
Interventional (in)direct Effects.” Biostatistics (in
press). https://doi.org/10.1093/biostatistics/kxac002.
Hejazi, Nima S, Mark J van der Laan, Holly E Janes, Peter B Gilbert, and
David C Benkeser. 2020. “Efficient Nonparametric Inference on the
Effects of Stochastic Interventions Under Two-Phase Sampling, with
Applications to Vaccine Efficacy Trials.” Biometrics. https://doi.org/10.1111/biom.13375.
Hernán, Miguel A, and James M Robins. 2022. Causal Inference:
What If. CRC Press.
Holland, Paul W. 1986. “Statistics and Causal Inference.”
Journal of the American Statistical Association 81 (396):
945–60.
Imai, Kosuke, Luke Keele, and Teppei Yamamoto. 2010.
“Identification, Inference and Sensitivity Analysis for Causal
Mediation Effects.” Statistical Science, 51–71.
Imbens, Guido W, and Donald B Rubin. 2015. Causal Inference in
Statistics, Social, and Biomedical Sciences. Cambridge University
Press.
Kennedy, Edward H. 2016. “Semiparametric Theory and Empirical
Processes in Causal Inference.” In Statistical Causal
Inferences and Their Applications in Public Health Research,
141–67. Springer.
———. 2019. “Nonparametric Causal Effects Based on Incremental
Propensity Score Interventions.” Journal of the American
Statistical Association 114 (526): 645–56.
Lok, Judith J. 2016. “Defining and Estimating Causal Direct and
Indirect Effects When Setting the Mediator to Specific Values Is Not
Feasible.” Statistics in Medicine 35 (22): 4008–20.
Luedtke, Alexander R, and Mark J van der Laan. 2016. “Optimal
Individualized Treatments in Resource-Limited Settings.” The
International Journal of Biostatisics 12 (1): 283–303. https://doi.org/10.1515/ijb-2015-0007.
Luedtke, Alex, and Mark J van der Laan. 2016. “Super-Learning of
an Optimal Dynamic Treatment Rule.” International Journal of
Biostatistics 12 (1): 305–32.
Montoya, Lina M, Mark J van der Laan, Alexander R Luedtke, Jennifer L
Skeem, Jeremy R Coyle, and Maya L Petersen. 2023. “The Optimal
Dynamic Treatment Rule Superlearner: Considerations, Performance, and
Application to Criminal Justice Interventions.” The
International Journal of Biostatistics 19 (1): 217–38. https://doi.org/10.1515/ijb-2020-0127.
Montoya, Lina M, Mark J van der Laan, Jennifer L Skeem, and Maya L
Petersen. 2023. “Estimators for the Value of the Optimal Dynamic
Treatment Rule with Application to Criminal Justice
Interventions.” The International Journal of
Biostatistics 19 (1): 239–59. https://doi.org/10.1515/ijb-2020-0128.
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.
Murphy, Susan A. 2003. “Optimal Dynamic Treatment Regimes.”
Journal of the Royal Statistical Society: Series B (Statistical
Methodology) 65 (2): 331–55.
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 (Anonymous). 2015a. “How Scientists Fool
Themselves — and How They Can Stop.” Nature 526 (7572).
———. 2015b. “Let’s Think about Cognitive Bias.”
Nature 526 (7572). https://doi.org/10.1038/526163a.
Neyman, Jerzy. 1938. “Contribution to the Theory of Sampling Human
Populations.” Journal of the American Statistical
Association 33 (201): 101–16.
Nguyen, Trang Quynh, Ian Schmid, and Elizabeth A Stuart. 2019.
“Clarifying Causal Mediation Analysis for the Applied Researcher:
Defining Effects Based on What We Want to Learn.” arXiv
Preprint arXiv:1904.08515.
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. 1995. “Causal Diagrams for Empirical
Research.” Biometrika 82 (4): 669–88.
———. 2001. “Direct and Indirect Effects.” arXiv
Preprint arXiv:1301.2300.
———. 2009. Causality: Models, Reasoning, and Inference.
Cambridge University Press.
———. 2010. “Brief Report: On the Consistency Rule in Causal
Inference: ‘Axiom, Definition, Assumption, or
Theorem?’” Epidemiology, 872–75.
Peng, Roger. 2015. “The Reproducibility Crisis in Science: A
Statistical Counterattack.” Significance 12 (3): 30–32.
Petersen, Maya L, Sandra E Sinisi, and Mark J van der Laan. 2006.
“Estimation of Direct Causal Effects.”
Epidemiology, 276–84.
Phillips, Rachael V, Mark J van der Laan, Hana Lee, and Susan Gruber.
2023. “Practical Considerations for Specifying a Super
Learner.” International Journal of Epidemiology. https://doi.org/10.1093/ije/dyad023.
Polley, Eric C, and Mark J van der Laan. 2010. “Super Learner in
Prediction.” Division of Biostatistics, University of
California, Berkeley; bepress.
Popper, Karl. 1934. The Logic of Scientific Discovery.
Routledge.
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.
R Core Team. 2021. “: A Language and Environment for Statistical
Computing.” Vienna, Austria: R Foundation for Statistical
Computing. https://www.R-project.org/.
Robins, James. 1986. “A New Approach to Causal Inference in
Mortality Studies with a Sustained Exposure Period—Application to
Control of the Healthy Worker Survivor Effect.” Mathematical
Modelling 7 (9): 1393–1512. https://doi.org/https://doi.org/10.1016/0270-0255(86)90088-6.
Robins, James M. 1986. “A New Approach to Causal Inference in
Mortality Studies with Sustained Exposure Periods — Application to
Control of the Healthy Worker Survivor Effect.” Mathematical
Modelling 7: 1393–1512.
———. 2004. “Optimal Structural Nested Models for Optimal
Sequential Decisions.” In Proceedings of the Second Seattle
Symposium in Biostatistics: Analysis of Correlated Data, 189–326.
Springer New York. https://doi.org/10.1007/978-1-4419-9076-1_11.
Robins, James M, and Sander Greenland. 1992. “Identifiability and
Exchangeability for Direct and Indirect Effects.”
Epidemiology, 143–55.
Robins, James M, and Thomas S Richardson. 2010. “Alternative
Graphical Causal Models and the Identification of Direct
Effects.” Causality and Psychopathology: Finding the
Determinants of Disorders and Their Cures, 103–58.
Robins, James, and Andrea Rotnitzky. 2014. “Discussion of
‘Dynamic Treatment Regimes: Technical Challenges and
Applications’.” Electron. J. Statist. 8 (1):
1273–89. https://doi.org/10.1214/14-EJS908.
Rubin, Donald B. 1978. “Bayesian Inference for Causal Effects: The
Role of Randomization.” The Annals of Statistics, 34–58.
———. 1980. “Randomization Analysis of Experimental Data: The
Fisher Randomization Test Comment.” Journal of the American
Statistical Association 75 (371): 591–93.
———. 2005. “Causal Inference Using Potential Outcomes: Design,
Modeling, Decisions.” Journal of the American Statistical
Association 100 (469): 322–31.
Rudolph, Kara E, Oleg Sofrygin, Wenjing Zheng, and Mark J van der Laan.
2017. “Robust and Flexible Estimation of Stochastic Mediation
Effects: A Proposed Method and Example in a Randomized Trial
Setting.” Epidemiologic Methods 7 (1).
Spirtes, Peter, Clark N Glymour, Richard Scheines, David Heckerman,
Christopher Meek, Gregory Cooper, and Thomas Richardson. 2000.
Causation, Prediction, and Search. MIT press.
Stark, Philip B, and Andrea Saltelli. 2018. “Cargo-Cult Statistics
and Scientific Crisis.” Significance 15 (4): 40–43.
Stock, James H. 1989. “Nonparametric Policy Analysis.”
Journal of the American Statistical Association 84 (406):
567–75.
Stromberg, Arnold et al. 2004. “Why Write Statistical Software?
The Case of Robust Statistical Methods.” Journal of
Statistical Software 10 (5): 1–8.
Sutton, Richard S, Andrew G Barto, et al. 1998. Introduction to
Reinforcement Learning. Vol. 135. MIT press Cambridge.
Szucs, Denes, and John Ioannidis. 2017. “When Null Hypothesis
Significance Testing Is Unsuitable for Research: A Reassessment.”
Frontiers in Human Neuroscience 11: 390.
Tchetgen Tchetgen, Eric J. 2013. “Inverse Odds Ratio-Weighted
Estimation for Causal Mediation Analysis.” Statistics in
Medicine 32 (26): 4567–80.
Tchetgen Tchetgen, Eric J, and Ilya Shpitser. 2012.
“Semiparametric Theory for Causal Mediation Analysis: Efficiency
Bounds, Multiple Robustness, and Sensitivity Analysis.”
Annals of Statistics 40 (3): 1816–45. https://doi.org/10.1214/12-AOS990.
Tchetgen Tchetgen, Eric J, and Tyler J VanderWeele. 2014. “On
Identification of Natural Direct Effects When a Confounder of the
Mediator Is Directly Affected by Exposure.” Epidemiology
25 (2): 282.
Textor, Johannes, Juliane Hardt, and Sven Knüppel. 2011.
“DAGitty: A Graphical Tool for Analyzing Causal
Diagrams.” Epidemiology 22 (5): 745.
Tofail, Fahmida, Lia CH Fernald, Kishor K Das, Mahbubur Rahman, Tahmeed
Ahmed, Kaniz K Jannat, Leanne Unicomb, et al. 2018. “Effect of
Water Quality, Sanitation, Hand Washing, and Nutritional Interventions
on Child Development in Rural Bangladesh (WASH Benefits Bangladesh): A
Cluster-Randomised Controlled Trial.” The Lancet Child &
Adolescent Health 2 (4): 255–68.
Tukey, John W. 1962. “The Future of Data Analysis.” The
Annals of Mathematical Statistics 33 (1): 1–67.
van der Laan, Mark J, and Sandrine Dudoit. 2003. “Unified
Cross-Validation Methodology for Selection Among Estimators and a
General Cross-Validated Adaptive Epsilon-Net Estimator: Finite Sample
Oracle Inequalities and Examples.” Division of
Biostatistics, University of California, Berkeley; bepress.
van der Laan, Mark J, Sandrine Dudoit, and Sunduz Keles. 2004.
“Asymptotic Optimality of Likelihood-Based
Cross-Validation.” Statistical Applications in Genetics and
Molecular Biology 3 (1): 1–23.
van der Laan, Mark J, and Alex Luedtke. 2015. “Targeted Learning
of the Mean Outcome Under an Optimal Dynamic Treatment Rule.”
Journal of Causal Inference 3 (1): 61–95.
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.
van der Vaart, Aad W, Sandrine Dudoit, and Mark J van der Laan. 2006.
“Oracle Inequalities for Multi-Fold Cross Validation.”
Statistics & Decisions 24 (3): 351–71.
VanderWeele, Tyler. 2015. Explanation in Causal Inference: Methods
for Mediation and Interaction. Oxford University Press.
VanderWeele, Tyler J, Stijn Vansteelandt, and James M Robins. 2014.
“Effect Decomposition in the Presence of an Exposure-Induced
Mediator-Outcome Confounder.” Epidemiology 25 (2): 300.
Vansteelandt, Stijn, Maarten Bekaert, and Theis Lange. 2012.
“Imputation Strategies for the Estimation of Natural Direct and
Indirect Effects.” Epidemiologic Methods 1 (1): 131–58.
Vansteelandt, Stijn, and Rhian M Daniel. 2017. “Interventional
Effects for Mediation Analysis with Multiple Mediators.”
Epidemiology 28 (2): 258.
Vansteelandt, Stijn, and Tyler J VanderWeele. 2012. “Natural
Direct and Indirect Effects on the Exposed: Effect Decomposition Under
Weaker Assumptions.” Biometrics 68 (4): 1019–27.
Wickham, Hadley. 2014. Advanced r. Chapman; Hall/CRC.
Wright, Sewall. 1934. “The Method of Path Coefficients.”
The Annals of Mathematical Statistics 5 (3): 161–215.
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.
Zhang, Baqun, Anastasios A Tsiatis, Marie Davidian, Min Zhang, and Eric
Laber. 2016. “Estimating Optimal Treatment Regimes from a
Classification Perspective.” Stat 5 (1): 278–78. https://doi.org/10.1002/sta4.124.
Zhao, Yingqi, Donglin Zeng, A John Rush, and Michael R Kosorok. 2012.
“Estimating Individualized Treatment Rules Using Outcome Weighted
Learning.” Journal of the American Statistical
Association 107 (499): 1106–18. https://doi.org/10.1080/01621459.2012.695674.
Zheng, Wenjing, and Mark J van der Laan. 2011. “Cross-Validated
Targeted Minimum-Loss-Based Estimation.” In Targeted
Learning: Causal Inference for Observational and Experimental Data,
edited by Mark J van der Laan and Sherri Rose, 459–74. Springer. https://doi.org/10.1007/978-1-4419-9782-1_27.
———. 2012. “Targeted Maximum Likelihood Estimation of Natural
Direct Effects.” International Journal of Biostatistics
8 (1). https://doi.org/10.2202/1557-4679.1361.