References

Bibliographic keys match the CDCS book file references.bib where possible, so chapters and package docs stay aligned. Prefer DOIs when citing externally.

Modified treatment policies and LMTP

Díaz Muñoz, I., & van der Laan, M. J. (2012). Population intervention causal effects based on stochastic interventions. Biometrics, 68(2), 541–549. doi:10.1111/j.1541-0420.2011.01685.x — key diaz2012stochastic
Díaz, I., Williams, N., Hoffman, K. L., & Schenck, E. J. (2023). Nonparametric causal effects based on longitudinal modified treatment policies. Journal of the American Statistical Association, 118(542), 846–857. doi:10.1080/01621459.2021.1955691 — key diaz2023lmtp
Williams, N. T., & Díaz, I. (2023). lmtp: An R package for estimating the causal effects of modified treatment policies. Observational Studies. muse.jhu.edu/article/883479 — key williams2023lmtp
Díaz, I., Hoffman, K. L., & Hejazi, N. S. (2024). Causal survival analysis under competing risks using longitudinal modified treatment policies. Lifetime Data Analysis, 30, 213–236. doi:10.1007/s10985-023-09606-7 — key diaz2024survival (future scope)

Targeted learning and Super Learner

van der Laan, M. J., & Rubin, D. (2006). Targeted maximum likelihood learning. The International Journal of Biostatistics, 2(1). — key vanderlaan2006targeted
van der Laan, M. J., Polley, E. C., & Hubbard, A. E. (2007). Super learner. Statistical Applications in Genetics and Molecular Biology, 6(1). — key vanderlaan2007super
van der Laan, M. J., & Rose, S. (2011). Targeted Learning: Causal Inference for Observational and Experimental Data. Springer. — key vanderlaan2011targeted
van der Laan, M. J., & Rose, S. (2018). Targeted Learning in Data Science. Springer. — key vanderlaan2018targeted
Schuler, M. S., & Rose, S. (2017). Targeted maximum likelihood estimation for causal inference in observational studies. American Journal of Epidemiology, 185(1), 65–73. — key schuler2017targeted
Zheng, W., & van der Laan, M. J. (2011). Cross-validated targeted minimum-loss-based estimation. In van der Laan & Rose (2011). — key zheng2011crossfitting
Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1–C68. doi:10.1111/ectj.12097 — key chernozhukov2018double

Mediation (natural, interventional, stochastic)

Robins, J. M., & Greenland, S. (1992). Identifiability and exchangeability for direct and indirect effects. Epidemiology, 3(2), 143–155. — key robins1992estimation
Pearl, J. (2001). Direct and indirect effects. In UAI. — key pearl2001direct
VanderWeele, T. J. (2015). Explanation in Causal Inference: Methods for Mediation and Interaction. Oxford University Press. — key vanderweele2015explanation
Vansteelandt, S., & Daniel, R. M. (2017). Interventional effects for mediation analysis with multiple mediators. Epidemiology, 28(2), 258–265. doi:10.1097/EDE.0000000000000596 — key vansteelandt2017interventional
Díaz, I., & Hejazi, N. S. (2020). Causal mediation analysis for stochastic interventions. Journal of the Royal Statistical Society: Series B, 82(3), 661–683. doi:10.1111/rssb.12362 — key diaz2020mediation
Hejazi, N. S., Rudolph, K. E., van der Laan, M. J., & Díaz, I. (2023). Nonparametric causal mediation analysis for stochastic interventional (in)direct effects. Biostatistics, 24(3), 686–707. doi:10.1093/biostatistics/kxac002 — key hejazi2023stochastic
Liu, R., Williams, N. T., Rudolph, K. E., & Díaz, I. (2024). General targeted machine learning for modern causal mediation analysis. arXiv:2408.14620. doi:10.48550/arXiv.2408.14620 — key liu2024mediation
Liu, R., Williams, N. T., Rudolph, K. E., & Díaz, I. (2025). crumble: A comprehensive framework for modern causal mediation analysis with intermediate confounding. arXiv:2604.09902. doi:10.48550/arXiv.2604.09902 — key liu2025crumble

Positivity, g-methods, and textbooks

Robins, J. (1986). A new approach to causal inference in mortality studies with a sustained exposure period. Mathematical Modelling, 7, 1393–1512. — key robins1986new
Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550–560. — key robins2000marginal
Petersen, M. L., Porter, K. E., Gruber, S., Wang, Y., & van der Laan, M. J. (2012). Diagnosing and responding to violations in the positivity assumption. Statistical Methods in Medical Research, 21(1), 31–54. doi:10.1177/0962280210386207 — key petersen2012positivity
Hernán, M. A., & Robins, J. M. (2020). Causal Inference: What If. Chapman & Hall/CRC. — key hernan2020causal

Sensitivity analysis

Cinelli, C., & Hazlett, C. (2020). Making sense of sensitivity: Extending omitted variable bias. Journal of the Royal Statistical Society: Series B, 82(1), 39–67. doi:10.1111/rssb.12348 — key cinelli2020sensitivity
VanderWeele, T. J., & Ding, P. (2017). Sensitivity analysis in observational research: introducing the E-value. Annals of Internal Medicine, 167(4), 268–274. — key vanderweele2017sensitivity
Rosenbaum, P. R. (2002). Observational Studies (2nd ed.). Springer. — key rosenbaum2002observational
Imai, K., Keele, L., & Yamamoto, T. (2010). Identification, inference, and sensitivity analysis for causal mediation effects. Statistical Science, 25(1), 51–71. — key imai2010identification

Structural identification (upstream: CausalDynamics)

Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge University Press. — key pearl2009causality
Shpitser, I., & Pearl, J. (2006). Identification of joint interventional distributions in recursive semi-Markovian causal models. In AAAI. — key shpitser2006identification
Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, Prediction, and Search (2nd ed.). MIT Press. — key spirtes2000causation

R packages lmtp and crumble — methodological companions cited above
CausalDynamics.jl — identification layer for this package
CDCS book — narrative companion with Quarto [@citekey] citations into the shared references.bib