References

Bishop, Y. M., Fienberg, S. E., & Holland, P. W. (1975). Discrete multivariate analysis. MIT Press.

Clifford, P., & Hammersley, J. (1971). Markov fields on finite graphs and lattices.

(unpublished manuscript)

Constantinou, P., & Dawid, A. P. (2017). Extended conditional independence and applications in causal inference. Annals of Statistics, 45(6), 2618–2653.

Darroch, J. N., Lauritzen, S. L., & Speed, T. P. (1980). Markov fields and log-linear interaction models for contingency tables. Annals of Statistics, 8(3), 522–539.

Dawid, A. P. (1979). Conditional independence in statistical theory. Journal of the Royal Statistical Society, Series B, 41(1), 1–15.

Deming, W. E., & Stephan, F. F. (1940). On a least squares adjustment of a sampled frequency table when the expected marginal totals are known. The Annals of Mathematical Statistics, 11(4), 427–444.

Dempster, A. P. (1972). Covariance selection. Biometrics, 28(1), 157–175.

Haberman, S. J. (1974). The analysis of frequency data. University of Chicago Press.

Henckel, L., Perković, E., & Maathuis, M. H. (2022). Graphical criteria for efficient total effect estimation via adjustment in causal linear models. Journal of the Royal Statistical Society, Series B, 84(2), 579–599.

Lauritzen, S. L. (1979). Lectures on contingency tables (Second). University of Aalborg Press.

Lauritzen, S. L., & Spiegelhalter, D. J. (1988). Local computations with probabilities on graphical structures and their application to expert systems. Journal of the Royal Statistical Society, Series B, 50(2), 157–194.

Pearl, J. (1985). Bayesian netwcrks: A model of self-activated memory for evidential reasoning. Proceedings of the 7th Conference of the Cognitive Science Society, 15–17.

Pearl, J. (1988). Probabilistic reasoning in intelligent systems. Morgan Kaufmann.

Pearl, J. (2000). Causality. Cambridge University Press.

Rotnitzky, A., & Smucler, E. (2020). Efficient adjustment sets for population average causal treatment effect estimation in graphical models. The Journal of Machine Learning Research, 21(1), 7642–7727.

Spirtes, P., Glymour, C. N., & Scheines, R. (1993). Causation, prediction, and search. Springer-Verlag.

Spohn, W. (1980). Stochastic independence, causal independence, and shieldability. Journal of Philosophical Logic, 9, 73–99.

Studený, M. (1992). Conditional independence relations have no finite complete characterization. Information Theory, Statistical Decision Functions and Random Processes. Transactions of the 11th Prague Conference Vol. B, 377–396.

Witte, J., Henckel, L., Maathuis, M. H., & Didelez, V. (2020). On efficient adjustment in causal graphs. Journal of Machine Learning Research, 21(1), 9956–10000.

Wright, S. (1921). Correlation and causation. Journal of Agricultural Research, 20, 557–585.

Wright, S. (1923). The theory of path coefficients—a reply to Niles’ criticism. Genetics, 8(3), 239.

Wright, S. (1934). The method of path coefficients. Annals of Mathematical Statistics, 5(3), 161–215.