R. Kohavi and B. Becker. UCI machine learning repository, 1996. URL http://archive.ics.uci.edu/ml/datasets/adult.
M. Kuroki. Selection of post-treatment variables for estimating total effect from empirical research. Journal of the Japan Statistical Society, 30(2):115-128, 2000.
M. Kuroki and Z. Cai. Selection of identifiability criteria for total effects by using path diagrams. arXiv preprint arXiv:1207.4140, 2012.
M. Mogstad and A. Torgovitsky. Identification and extrapolation of causal effects with instrumental variables. Annual Review of Economics, 10:577-613, 2018.
R. Nabi, D. Malinsky, and I. Shpitser. Learning optimal fair policies. In International Conference on Machine Learning, pages 4674-4682. PMLR, 2019.
J. Pearl. [bayesian analysis in expert systems]: Comment: graphical models, causality and intervention. Statistical Science, 8(3):266-269, 1993.
J. Pearl. Causal diagrams for empirical research. Biometrika, 82(4):669-688, 1995.
J. Pearl. Causality. Cambridge university press, 2009.
E. Perkovic, J. Textor, M. Kalisch, and M. H. Maathuis. Complete graphical characterization and construction of adjustment sets in markov equivalence classes of ancestral graphs. The Journal of Machine Learning Research, 2018.
D. Plecko and E. Baireinboim. Causal fairness analysis. arXiv preprint arXiv:2207.11385, 2022.
A. Shah, K. Shanmugam, and K. Ahuja. Finding valid adjustments under non-ignorability with minimal dag knowledge. In International Conference on Artificial Intelligence and Statistics, pages 5538-5562. PMLR, 2022.
I. Shpitser and J. Pearl. Identification of joint interventional distributions in recursive semi-markovian causal models. In Proceedings of the National Conference on Artificial Intelligence, volume 21, page 1219. Menlo Park, CA; Cambridge, MA; London; AAAI Press; MIT Press; 1999, 2006.
P. Spirtes, C. N. Glymour, and R. Scheines. Causation, prediction, and search. MIT press, 2000.
E. V. Strobl, P. L. Spirtes, and S. Visweswaran. Estimating and controlling the false discovery rate for the pc algorithm using edge-specific p-values. arXiv preprint arXiv:1607.03975, 2016.
E. V. Strobl, P. L. Spirtes, and S. Visweswaran. Estimating and controlling the false discovery rate of the pc algorithm using edge-specific p-values. ACM Transactions on Intelligent Systems and Technology (TIST), 10(5):1-37, 2019a.
E. V. Strobl, K. Zhang, and S. Visweswaran. Approximate kernel-based conditional independence tests for fast non-parametric causal discovery. Journal of Causal Inference, 7(1), 2019b.
J. Tian and J. Pearl. A general identification condition for causal effects. eScholarship, University of California, 2002.
S. Triantafillou and I. Tsamardinos. Constraint-based causal discovery from multiple interventions over overlapping variable sets. The Journal of Machine Learning Research, 16(1):2147-2205, 2015.
V. Veitch and A. Zaveri. Sense and sensitivity analysis: Simple post-hoc analysis of bias due to unobserved confounding. Advances in Neural Information Processing Systems, 33:10999-11009, 2020.
T. Verma and J. Pearl. Causal networks: Semantics and expressiveness. In Machine intelligence and pattern recognition, volume 9, pages 69-76. Elsevier, 1990.
M. Wienöbst, B. van der Zander, and M. Liskiewicz. Finding front-door adjustment sets in linear time. arXiv preprint arXiv:2211.16468, 2022.