Abstract
The kind of causal inference seen in natural human thought can be "algorithmitized" to help produce human-level machine intelligence.
- Balke, A. and Pearl, J. Probabilistic evaluation of counterfactual queries. In Proceedings of the 12<sup>th</sup> National Conference on Artificial Intelligence (Seattle, WA, July 31-Aug. 4). MIT Press, Menlo Park, CA, 1994, 230--237. Google ScholarDigital Library
- Bareinboim, E. and Pearl, J. Causal inference by surrogate experiments: z-identifiability. In Proceedings of the 28<sup>th</sup> Conference on Uncertainty in Artificial Intelligence, N. de Freitas and K. Murphy, Eds. (Catalina Island, CA, Aug. 14--18). AUAI Press, Corvallis, OR, 2012, 113--120. Google ScholarDigital Library
- Bareinboim, E. and Pearl, J. Causal inference and the data-fusion problem. Proceedings of the National Academy of Sciences 113, 27 (2016), 7345--7352.Google ScholarCross Ref
- Chen, Z. and Liu, B. Lifelong Machine Learning. Morgan and Claypool Publishers, San Rafael, CA, 2016. Google ScholarDigital Library
- Darwiche, A. Human-Level Intelligence or Animal-Like Abilities? Technical Report. Department of Computer Science, University of California, Los Angeles, CA, 2017; https://arxiv.org/pdf/1707.04327.pdfGoogle Scholar
- Graham, J. Missing Data: Analysis and Design (Statistics for Social and Behavioral Sciences). Springer, 2012.Google ScholarCross Ref
- Halpern, J.H. and Pearl, J. Causes and explanations: A structural-model approach: Part I: Causes. British Journal of Philosophy of Science 56 (2005), 843--887.Google ScholarCross Ref
- Hutson, M. AI researchers allege that machine learning is alchemy. Science (May 3, 2018); https://www.sciencemag.org/news/2018/05/ai-researchers-allege-machine-learning-alchemyGoogle Scholar
- Jaber, A., Zhang, J.J., and Bareinboim, E. Causal identification under Markov equivalence. In Proceedings of the 34<sup>th</sup> Conference on Uncertainty in Artificial Intelligence, A. Globerson and R. Silva, Eds. (Monterey, CA, Aug. 6--10). AUAI Press, Corvallis, OR, 2018, 978--987.Google Scholar
- Lake, B.M., Salakhutdinov, R., and Tenenbaum, J.B. Human-level concept learning through probabilistic program induction. Science 350, 6266 (Dec. 2015), 1332--1338.Google ScholarCross Ref
- Marcus, G. Deep Learning: A Critical Appraisal. Technical Report. Departments of Psychology and Neural Science, New York University, New York, 2018; https://arxiv.org/pdf/1801.00631.pdfGoogle Scholar
- Mohan, K. and Pearl, J. Graphical Models for Processing Missing Data. Technical Report R-473. Department of Computer Science, University of California, Los Angeles, CA, 2018; forthcoming, Journal of American Statistical Association; http://ftp.cs.ucla.edu/pub/stat_ser/r473.pdfGoogle Scholar
- Mohan, K., Pearl, J., and Tian, J. Graphical models for inference with missing data. In Advances in Neural Information Processing Systems 26, C.J.C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K.Q. Weinberger, Eds. Curran Associates, Inc., Red Hook, NY, 2013, 1277--1285; http://papers.nips.cc/paper/4899-graphical-models-for-inference-with-missing-data.pdf Google ScholarDigital Library
- Morgan, S.L. and Winship, C. Counterfactuals and Causal Inference: Methods and Principles for Social Research (Analytical Methods for Social Research), Second Edition. Cambridge University Press, New York, 2015.Google Scholar
- Pearl, J. Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, San Mateo, CA, 1988. Google ScholarDigital Library
- Pearl, J. Comment: Graphical models, causality, and intervention. Statistical Science 8, 3 (1993), 266--269.Google ScholarCross Ref
- Pearl, J. Causal diagrams for empirical research. Biometrika 82, 4 (Dec. 1995), 669--710.Google ScholarCross Ref
- Pearl, J. Causality: Models, Reasoning, and Inference. Cambridge University Press, New York, 2000; Second Edition, 2009. Google ScholarDigital Library
- Pearl, J. Direct and indirect effects. In Proceedings of the 17<sup>th</sup> Conference on Uncertainty in Artificial Intelligence (Seattle, WA, Aug. 2--5). Morgan Kaufmann, San Francisco, CA, 2001, 411--420. Google ScholarDigital Library
- Pearl, J. Causes of effects and effects of causes. Journal of Sociological Methods and Research 44, 1 (2015a), 149--164.Google Scholar
- Pearl, J. Trygve Haavelmo and the emergence of causal calculus. Econometric Theory 31, 1 (2015b), 152--179; special issue on Haavelmo centennialGoogle ScholarCross Ref
- Pearl, J. and Bareinboim, E. External validity: From do-calculus to transportability across populations. Statistical Science 29, 4 (2014), 579--595.Google ScholarCross Ref
- Pearl, J. and Mackenzie, D. The Book of Why: The New Science of Cause and Effect. Basic Books, New York, 2018. Google ScholarDigital Library
- Peters, J., Janzing, D. and Schölkopf, B. Elements of Causal Inference: Foundations and Learning Algorithms. MIT Press, Cambridge, MA, 2017. Google ScholarDigital Library
- Porta, M. The deconstruction of paradoxes in epidemiology. OUPblog, Oct. 17, 2014; https://blog.oup.com/2014/10/deconstruction-paradoxes-sociology-epidemiology/Google Scholar
- Ribeiro, M.T., Singh, S., and Guestrin, C. Why should I trust you?: Explaining the predictions of any classifier. In Proceedings of the 22<sup>nd</sup> ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (San Francisco, CA, Aug. 13--17). ACM Press, New York, 2016, 1135--1144. Google ScholarDigital Library
- Robins, J.M. and Greenland, S. Identifiability and exchangeability for direct and indirect effects. Epidemiology 3, 2 (Mar. 1992), 143--155.Google ScholarCross Ref
- Rosenbaum, P. and Rubin, D. The central role of propensity score in observational studies for causal effects. Biometrika 70, 1 (Apr. 1983), 41--55.Google ScholarCross Ref
- Shimizu, S., Hoyer, P.O., Hyvärinen, A., and Kerminen, A.J. A linear non-Gaussian acyclic model for causal discovery. Journal of the Machine Learning Research 7 (Oct. 2006), 2003--2030. Google ScholarDigital Library
- Shpitser, I. and Pearl, J. Complete identification methods for the causal hierarchy. Journal of Machine Learning Research 9 (2008), 1941--1979. Google ScholarDigital Library
- Spirtes, P., Glymour, C.N., and Scheines, R. Causation, Prediction, and Search, Second Edition. MIT Press, Cambridge, MA, 2000.Google Scholar
- Tian, J. and Pearl, J. A general identification condition for causal effects. In Proceedings of the 18<sup>th</sup> National Conference on Artificial Intelligence (Edmonton, AB, Canada, July 28-Aug. 1). AAAI Press/MIT Press, Menlo Park, CA, 2002, 567--573. Google ScholarDigital Library
- van der Laan, M.J. and Rose, S. Targeted Learning: Causal Inference for Observational and Experimental Data. Springer, New York, 2011.Google ScholarCross Ref
- VanderWeele, T.J. Explanation in Causal Inference: Methods for Mediation and Interaction. Oxford University Press, New York, 2015.Google Scholar
- Zhang, J. and Bareinboim, E. Transfer learning in multi-armed bandits: A causal approach. In Proceedings of the 26<sup>th</sup> International Joint Conference on Artificial Intelligence (Melbourne, Australia, Aug. 19--25). AAAI Press, Menlo Park, CA, 2017, 1340--1346. Google ScholarDigital Library
Index Terms
- The seven tools of causal inference, with reflections on machine learning
Recommendations
Causal Inference Meets Machine Learning
KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data MiningCausal inference has numerous real-world applications in many domains such as health care, marketing, political science and online advertising. Treatment effect estimation, a fundamental problem in causal inference, has been extensively studied in ...
Causal Learning with Occam’s Razor
AbstractOccam’s razor directs us to adopt the simplest hypothesis consistent with the evidence. Learning theory provides a precise definition of the inductive simplicity of a hypothesis for a given learning problem. This definition specifies a learning ...
Causal Inference and Causal Machine Learning with Practical Applications: The paper highlights the concepts of Causal Inference and Causal ML along with different implementation techniques
CODS-COMAD '23: Proceedings of the 6th Joint International Conference on Data Science & Management of Data (10th ACM IKDD CODS and 28th COMAD)One of the most important research areas in Machine Learning is to build prescriptive models. This requires understanding and measurement of the causal impact of any proposed treatment, followed by designing optimal strategy based on such causal ...
Comments