Suggested further readings

First, there are a number of different perspectives on causality from multiple communities. Highly recommendable are:

Statistics:

Hernan, M. A., & Robins J. M. (2019) Causal inference: What If. (Beautifully applied and with code samples.)

Pearl, J., & Mackenzie, D. (2018). The book of why: the new science of cause and effect. Basic books. (Super readable book on why causality matters so much. Not overly charitable about other communities.)

Pearl, J. (2009). Causality. Cambridge university press. (Foundational book for causal inference, DAG style and do- operators.)

Econometrics:

Angrist, J. D., & Pischke, J. S. (2014). Mastering’metrics: The path from cause to effect. Princeton university press. (Very readable book for practical causal inference.)

Angrist, J. D., & Pischke, J. S. (2008). Mostly harmless econometrics. Princeton university press. (Beautiful book highlighting the ways we can use real world data to get at causal estimates with strong computational treatments.)

Imbens, G. W., & Rubin, D. B. (2015). Causal inference in statistics, social, and biomedical sciences. Cambridge University Press. (Another very broad book.)

Epidemiology:

Aschengrau, A., & Seage, G. R. (2013). Essentials of epidemiology in public health. Jones & Bartlett Publishers. (This book shows how in epidemiology causality is often even harder than thought.)

Machine learning:

Jonas, P., Dominik, J., & Bernhard, S. (2017). Elements of causal inference: foundations and learning algorithms. (This book combines Pearl type approaches with new ML inspired contributions.)

A broad range of relevant papers:

Cooper, G. F., & Herskovits, E. (1992). A Bayesian method for the induction of probabilistic networks from data. Machine learning, 9(4), 309-347. doi: 10.1007/BF00994110 .

Kass, R. E., Eden, U. T., & Brown, E. N. (2014). Analysis of neural data (Vol. 491). New York: Springer. (Another Kass book that focuses on data analysis.)

Kass, R. E., Amari, S. I., Arai, K., Brown, E. N., Diekman, C. O., Diesmann, M., … & Kramer, M. A. (2018). Computational neuroscience: Mathematical and statistical perspectives. Annual review of statistics and its application, 5, 183-214. doi: 10.1146/annurev-statistics-041715-033733 (postprint: dspace.mit.edu/bitstream/1721.1/126718/2/AnnRev2017final.pdf ). Intro to computational neuroscience ideas.

Marinescu, I. E., Lawlor, P. N., & Kording, K. P. (2018). Quasi-experimental causality in neuroscience and behavioural research. Nature human behaviour, 2(12), 891-898. doi: 10.1038/s41562-018-0466-5 . A broad overview of econ style/ quasiexperimental causality for neuroscience

Mooij, J. M., Peters, J., Janzing, D., Zscheischler, J., & Schölkopf, B. (2016). Distinguishing cause from effect using observational data: methods and benchmarks. The Journal of Machine Learning Research, 17(1), 1103-1204. URL: https://dl.acm.org/doi/10.5555/2946645.2946677

Peters, J., Bühlmann, P., & Meinshausen, N. (2016). Causal inference by using invariant prediction: identification and confidence intervals. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 78(5), 947-1012. doi: 10.1111/rssb.12167 (preprint: arxiv.org/pdf/1501.01332 ).

Scholkopf, B. (2022). Causality for machine learning. In Probabilistic and Causal Inference: The Works of Judea Pearl (pp. 765-804). doi: 10.1145/3501714.3501755 (preprint: arxiv.org/pdf/1911.10500 ). Discussing the role of causality for machine learning.

Shimizu, S., Hoyer, P. O., Hyvärinen, A., Kerminen, A., & Jordan, M. (2006). A linear non-Gaussian acyclic model for causal discovery. Journal of Machine Learning Research, 7(10). URL: jmlr.org/papers/v7/shimizu06a.html.

Spirtes, P., Glymour, C. N., Scheines, R., & Heckerman, D. (2000). Causation, prediction, and search. MIT press.

Triantafillou, S., & Tsamardinos, I. (2015). Constraint-based causal discovery from multiple interventions over overlapping variable sets. The Journal of Machine Learning Research, 16(1), 2147-2205. URL: jmlr.org/papers/v16/triantafillou15a.html.