---
title: "Suggested further readings"
source: Neuromatch Computational Neuroscience
source_url: https://github.com/NeuromatchAcademy/course-content
licence: CC-BY-4.0
domain: foundations
subdomain: neuromatch-compneuro
date_added: 2026-04-25
---

# Suggested further readings

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

Highly recommendable are:

### Statistics:

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._

### Econometrics:

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_

### Epidemiology:

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](https://doi.org/10.1007/BF00994110) {{ open_access }}.

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](https://doi.org/10.1146/annurev-statistics-041715-033733) {{ closed_access }} (postprint: [dspace.mit.edu/bitstream/1721.1/126718/2/AnnRev2017final.pdf](https://dspace.mit.edu/bitstream/1721.1/126718/2/AnnRev2017final.pdf) {{ open_access }}). _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](https://doi.org/10.1038/s41562-018-0466-5) {{ closed_access }}. _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: [dl.acm.org/doi/10.5555/2946645.2946677](https://dl.acm.org/doi/10.5555/2946645.2946677)

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](https://doi.org/10.1111/rssb.12167) {{ closed_access }} (preprint: [arxiv:1501.01332](http://arxiv.org/abs/1501.01332) {{ open_access }}).

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](https://doi.org/10.1145/3501714.3501755) {{ closed_access }} (preprint: [arxiv:1911.10500](http://arxiv.org/abs/1911.10500) {{ open_access }}). _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: [jmlr.org/papers/v7/shimizu06a.html](https://www.jmlr.org/papers/v7/shimizu06a.html).

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: [jmlr.org/papers/v16/triantafillou15a.html](https://www.jmlr.org/papers/v16/triantafillou15a.html).
