references.Rmd

# References {-}

## Required references {-}

- **[PRIMER]**: Causal Inference in Statistics: A Primer, by Judea Pearl, Madelyn Glymour, and Nicholas P. Jewell. PDF freely available [online](http://bayes.cs.ucla.edu/PRIMER/).
- **[WHATIF]**: Causal Inference: What If, by Miguel A. Hernán and James M. Robins. PDF freely available [online](https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/).


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## References for review {-}

- [STAT 155 Notes](https://bcheggeseth.github.io/Stat155Notes/): An online set of notes for STAT 155 written by the Macalester statistics faculty.
- [Probability Essentials](https://youtu.be/acofPsJDq5Y): A YouTube video outlining some key ideas from probability that we will use in this course.
- [This article](https://medium.com/@EpiEllie/having-confidence-in-confidence-intervals-8f881712d837) by Eleanor Murray has a great explanation of the most common misinterpretation of confidence intervals.

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## Further exploration {-}

- The Book of Why: The New Science of Cause and Effect, by Judea Pearl and Dana Mackenzie ([Amazon](https://www.amazon.com/Book-Why-Science-Cause-Effect/dp/046509760X))
- Causality: Models, Reasoning, and Inference, by Judea Pearl (available as an [eBook](http://linker2.worldcat.org/?jHome=https%3A%2F%2Fgo.openathens.net%2Fredirector%2Fmacalester.edu%3Furl%3Dhttps%3A%2F%2Fdoi.org%2F10.1017%2FCBO9780511803161&linktype=best) through Macalester's library)
- edX course: [Causal Diagrams: Draw Your Assumptions Before Your Conclusions](https://www.edx.org/course/causal-diagrams-draw-your-assumptions-before-your)
- Udemy course: [Causal Data Science with Directed Acyclic Graphs](https://www.udemy.com/course/causal-data-science/)