# Written resources on causal inference

Below is a list of written resources (books, blog posts, etc.) that are useful for learning about causal inference.

## Quasi-experiment resources

* Angrist, J. D., & Pischke, J. S. (2009). [Mostly harmless econometrics: An empiricist's companion](https://www.mostlyharmlesseconometrics.com). Princeton university press.
* Angrist, J. D., & Pischke, J. S. (2014). [Mastering'metrics: The path from cause to effect](https://www.masteringmetrics.com). Princeton University Press.
* Cunningham, S. (2021). [Causal inference: The Mixtape](https://mixtape.scunning.com). Yale University Press.
* Huntington-Klein, N. (2021). [The effect: An introduction to research design and causality](https://theeffectbook.net). Chapman and Hall/CRC.
* Reichardt, C. S. (2019). Quasi-experimentation: A guide to design and analysis. Guilford Publications.

## Bayesian causal inference resources
* The official [PyMC examples gallery](https://www.pymc.io/projects/examples/en/latest/gallery.html) has a set of examples specifically relating to causal inference.

## General causal inference resources

* [Awesome Causal Inference](https://github.com/matteocourthoud/awesome-causal-inference), a curated list of resources on causal inference, including books, blogs, and tutorials.