Suggested further readings

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

Highly recommendable are:


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

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

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


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

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

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


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

Machine learning:

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

A broad range of relevant papers:

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

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

Kass, R. E., Amari, S. I., Arai, K., Brown, E. N., Diekman, C. O., Diesmann, M., … and 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 Closed Access publication (postprint: Open Access publication). Note: Intro to computational neuroscience ideas.

Marinescu, I. E., Lawlor, P. N., and 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 Closed Access publication. Note: A broad overview of econ style/ quasiexperimental causality for neuroscience.

Mooij, J. M., Peters, J., Janzing, D., Zscheischler, J., and Schölkopf, B. (2016). Distinguishing cause from effect using observational data: methods and benchmarks. The Journal of Machine Learning Research 17(1): 1103-1204. url:

Peters, J., Bühlmann, P., and 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 Closed Access publication (preprint: arxiv:1501.01332 Open Access publication).

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 Closed Access publication (preprint: arxiv:1911.10500 Open Access publication). Note: Discussing the role of causality for machine learning.

Shimizu, S., Hoyer, P. O., Hyvärinen, A., Kerminen, A., and Jordan, M. (2006). A linear non-Gaussian acyclic model for causal discovery. Journal of Machine Learning Research 7(10). url:

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

Triantafillou, S., and 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: