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References

Literature underpinning CausalDynamics.jl. BibTeX keys match the CDCS book references.bib where possible. For estimation (LMTP, mediation TMLE, Super Learner), see also CausalTargeted.jl references.

Adjustment-set algorithms (CausalInference / gensearch)

CausalDynamics delegates backdoor and frontdoor adjustment to CausalInference.jl gensearch. Literature underpinning those algorithms:

  • Shpitser, I., VanderWeele, T., & Robins, J. M. (2010). On the validity of covariate adjustment for estimating causal effects. In UAI. — key shpitser2010validity (arXiv:1203.3515)

  • van der Zander, B., Liśkiewicz, M., & Textor, J. (2019). Separators and adjustment sets in causal graphs: complete criteria and an algorithmic framework. Artificial Intelligence, 270, 1–40. — key vanderzander2019separators (arXiv:1803.00116)

  • Wienöbst, M., van der Zander, B., & Liśkiewicz, M. (2024). Linear-time algorithms for front-door adjustment in causal graphs. In AAAI. — key wienoebst2024lineartimefrontdoor (arXiv:2211.16468)

Shared BibTeX keys also appear in the CDCS book references.bib.

Structural causality and identification

  • Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge University Press. — key pearl2009causality

  • Pearl, J. (2018). The Book of Why. Basic Books. — key pearl2018bookofwhy

  • 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

  • Peters, J., Janzing, D., & Schölkopf, B. (2017). Elements of Causal Inference. MIT Press. — key peters2017elements

  • Bareinboim, E., & Pearl, J. (2016). Causal inference and the data-fusion problem. Proceedings of the National Academy of Sciences, 113(27), 7345–7352. — key bareinboim2016causal

  • Imbens, G. W., & Rubin, D. B. (2015). Causal Inference for Statistics, Social, and Biomedical Sciences. Cambridge University Press. — key imbens2015causal

g-methods and longitudinal confounding

  • 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

  • Hernán, M. A., & Robins, J. M. (2020). Causal Inference: What If. Chapman & Hall/CRC. — key hernan2020causal

Time-indexed / dynamical graphs

Unrolled lag DAGs (TemporalDAGSpec, unroll_temporal_dag, TemporalEffectQuery) apply standard backdoor criteria on an expanded static graph. Conceptual links:

  • Pearl (2009), Ch. on dynamic models / time-indexed SCMs
  • Discrete-time CDMs in the CDCS book (Ch. 28)
  • Estimation of time-indexed MTP effects: Díaz et al. (2023), JASA — key diaz2023lmtp (implemented in CausalTargeted)

Mediation identification (structural)

  • Pearl, J. (2001). Direct and indirect effects. In UAI. — key pearl2001direct

  • Robins, J. M., & Greenland, S. (1992). Identifiability and exchangeability for direct and indirect effects. Epidemiology. — key robins1992estimation

  • Avin, C., Shpitser, I., & Pearl, J. (2005). Identifiability of path-specific effects. In IJCAI. — key avin2005identifiability

  • VanderWeele, T. J. (2015). Explanation in Causal Inference. Oxford University Press. — key vanderweele2015explanation

Discovery (optional Associations.jl bridge)

  • Spirtes et al. (2000) — PC and constraint-based search
  • Chickering, D. M. (2002). Optimal structure identification with greedy search. JMLR. — key chickering2002optimal
  • Runge, J., et al. (2019). Detecting and quantifying causal associations in large nonlinear time series datasets. Science Advances. — key runge2019detecting

Discovery outputs feed sensitivity comparisons in CausalTargeted (discovery_adjustment_sensitivity); they must not silently replace a user DAG.

  • Graphs.jl — graph data structures
  • CausalInference.jl — d-separation / backdoor façades used internally
  • CausalTargeted.jl — LMTP / mediation estimation consuming IdentificationResult
  • DAGMakie.jl — DAG visualisation
  • Associations.jl — optional discovery bridge
  • ModelingToolkit.jl / Symbolics.jl — symbolic modelling (SciML integration docs)

Further reading

For narrative treatment with Quarto [@citekey] citations into the shared bibliography, see the CDCS Book.