Angrist, J. D., Imbens, G. W., and Rubin, D. B. (1996). “Identification of Causal Effects Using Instrumental Variables.” Journal of the American Statistical Association 91:444–455.
Elwert, F. (2013). “Graphical Causal Models.” In Handbook of Causal Analysis for Social Research, edited by S. L. Morgan, 245–273. Dordrecht: Springer.
Elwert, F., and Winship, C. (2014). “Endogenous Selection Bias: The Problem of Conditioning on a Collider Variable.” Annual Review of Sociology 40:31–53.
Galles, D., and Pearl, J. (1998). “An Axiomatic Characterization of Causal Counterfactuals.” Foundations of Science 3:151–182.
Geiger, D., and Pearl, J. (1988). “On the Logic of Causal Models.” In Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence, edited by R. D. Shacter, T. S. Levitt, L. N. Kanal, and J. F. Lemmer, 136–147. Amsterdam: North-Holland.
Greenland, S., Pearl, J., and Robins, J. M. (1999). “Causal Diagrams for Epidemiologic Research.” Epidemiology 10:37–48.
Imbens, G. W. (2014). “Instrumental Variables: An Econometrician’s Perspective.” Statistical Science 29:323–358.
Koller, D., and Friedman, N. (2009). Probabilistic Graphical Models: Principles and Techniques. Cambridge, MA: MIT Press.
Lauritzen, S. L. (1996). Graphical Models. Oxford: Clarendon Press.
Neyman, J., Dabrowska, D. M., and Speed, T. P. (1990). “On the Application of Probability Theory to Agricultural Experiments: Essay on Principles, Section 9.” Statistical Science 5:465–472. Translated and edited by Dabrowska and Speed from the Polish original by Neyman (1923).
Pearl, J. (1993). “Comment: Graphical Models, Causality and Intervention.” Statistical Science 8:266–269.
Pearl, J. (2009a). “Causal Inference in Statistics: An Overview.” Statistics Surveys 3:96–146.
Pearl, J. (2009b). Causality: Models, Reasoning, and Inference. 2nd ed. Cambridge: Cambridge University Press.
Pearl, J. (2010). “An Introduction to Causal Inference.” International Journal of Biostatistics 6:1–62.
Pearl, J. (2012). “The Causal Foundations of Structural Equation Modeling.” In Handbook of Structural Equation Modeling, edited by R. H. Hoyle, 68–91. New York: Guilford Press.
Pearl, J., and Verma, T. (1987). “The Logic of Representing Dependencies by Directed Graphs.” In Proceedings of the Sixth National Conference on Artificial Intelligence, 374–379. AAAI Press.
Perković, E., Textor, J., Kalisch, M., and Maathuis, M. (2018). “Complete Graphical Characterization and Construction of Adjustment Sets in Markov Equivalence Classes of Ancestral Graphs.” Journal of Machine Learning Research 18:1–62.
Rubin, D. B. (1980). “Comment on D. Basu, 'Randomization Analysis of Experimental Data: The Fisher Randomization Test'.” Journal of the American Statistical Association 75:591–593.
Rubin, D. B. (1990). “Comment: Neyman (1923) and Causal Inference in Experiments and Observational Studies.” Statistical Science 5:472–480.
Schafer, J. L., and Kang, J. (2008). “Average Causal Effects from Nonrandomized Studies: A Practical Guide and Simulated Example.” Psychological Methods 13:279–313.
Shpitser, I., VanderWeele, T., and Robins, J. M. (2010). “On the Validity of Covariate Adjustment for Estimating Causal Effects.” In Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence, edited by P. Grünwald and P. Spirtes, 527–536. Corvallis, OR: AUAI Press.
Spirtes, P., Glymour, C., and Scheines, R. (2001). Causation, Prediction, and Search. 2nd ed. Cambridge, MA: MIT Press.
Takata, K. (2010). “Space-Optimal, Backtracking Algorithms to List the Minimal Vertex Separators of a Graph.” Discrete Applied Mathematics 158:1660–1667.
Thornley, S., Marshall, R. J., Jackson, R., Gentles, D., Dalbeth, N., Crengle, S., Kerr, A., and Wells, S. (2013). “Is Serum Urate Causally Associated with Incident Cardiovascular Disease?” Rheumatology 52:135–142.
Timmermann, C. A. G., Budtz-Jørgensen, E., Petersen, M. S., Weihe, P., Steuerwald, U., Nielsen, F., Jensen, T. K., and Grandjean, P. (2017). “Shorter Duration of Breastfeeding at Elevated Exposures to Perfluoroalkyl Substances.” Reproductive Toxicology 68:164–170.
Van der Zander, B., Liśkiewicz, M., and Textor, J. (2014). “Constructing Separators and Adjustment Sets in Ancestral Graphs.” In Proceedings of the Thirtieth Conference on Causal Inference: Learning and Prediction, edited by N. L. Zhang and J. Tian, 11–24. Corvallis, OR: AUAI Press.
Van der Zander, B., Textor, J., and Liśkiewicz, M. (2015). “Efficiently Finding Conditional Instruments for Causal Inference.” In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, edited by Q. Yang and M. Wooldridge, 3242–3249. Palo Alto, CA: AAAI Press.