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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 (SurvivalPolicy / run_survival_lmtp; competing risks deferred)

  • Rosenblum, M., & van der Laan, M. J. (2010). Targeted Maximum Likelihood Estimation of the Parameter of a Marginal Structural Model. The International Journal of Biostatistics, 6(2). doi:10.2202/1557-4679.1238 — key rosenblum2010msm (run_repeated_outcome_msm / run_parametric_repeated_msm)

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

  • Phillips, R. V., van der Laan, M. J., Lee, H., & Gruber, S. (2023). Practical considerations for specifying a super learner. International Journal of Epidemiology, 52(4), 1276–1285. doi:10.1093/ije/dyad023 — key phillips2023super (ensemble SL vs discrete SL / :cv_selector)

  • 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

Benchmark and stress-cohort data

Used by the application stress harness (see Stress validation); not bundled inside this package.

  • Liu, W., McNeilly, T. N., Mitchell, M., Burgess, S. T. G., Nisbet, A. J., Matthews, J. B., & Babayan, S. A. (2022). Vaccine-induced time- and age-dependent mucosal immunity to gastrointestinal parasite infection. npj Vaccines, 7, 78. doi:10.1038/s41541-022-00501-0 — CircVax sheep phenotype (SimonAB/Liu2022)
  • Hill, J. L. (2011). Bayesian nonparametric modeling for causal inference. Journal of Computational and Graphical Statistics, 20(1), 217–240 — IHDP NPCI construction; CEVAE mirror AMLab-Amsterdam/CEVAE
  • Louizos, C., Shalit, U., Mooij, J., Sontag, D., Zemel, R., & Welling, M. (2017). Causal effect inference with deep latent-variable models. NeurIPS — IHDP / Twins mirrors
  • LaLonde, R. J. (1986). Evaluating the econometric evaluations of training programs with experimental data. American Economic Review, 76(4), 604–620 — NSW experiment (MatchIt::lalonde via Rdatasets)
  • Dehejia, R. H., & Wahba, S. (1999). Causal effects in nonexperimental studies… JASA — CPS observational counterpart (causaldata::cps_mixtape)
  • Imai, K., Keele, L., & Tingley, D. (2010). A general approach to causal mediation analysis. Psychological Methods — JOBS II (mediation::jobs)