Going beyond

The “causal inference revolution” means our course materials are now commonly taught not only at education schools but also in other social sciences. They are also highly sought after by employers.

Here are additional resources in case you would like to go beyond what we cover in class. You may also find it helpful to see someone else explain the same concept.

Other courses

  1. Andrew Heiss teaches phenomenal classes at Georgia State University. In fact, much of our course builds on his materials. Go here if you would like to review some of the materials we covered in class.

  2. Fiona Burlig teaches a great program evaluation class at the University of Chicago. During the pandemic, she put all of her lectures online. Go here if you are looking for a slightly more advanced class.

  3. Matt Blackwell provides a great introduction to data science for the social sciences. Go here if you are looking for a broader introduction to data analysis with R.

  4. Paul Goldsmith-Pinkham teaches this applied methods class at Yale—it looks great! All of his lecture recordings are online here.

  5. Within the PhD program at UCI’s School of Education, EDUC 287A (Advanced Quantitative Data Analysis for Causal Inference) and EDUC 287B (Causal Inference: Methods for Program Evaluation and Policy Research) build on our class.

Other books

To recap, here are the main books we used in class:

Then, we also covered a few chapters from the following books:

We did not cover three additional books–all three are great, but they are slightly more math heavy (esp. the last one).

If you are intrigued, there are many more excellent (more technical) books on causal inference, including this one by Imbens and Rubin, this “classic” by Jeff Wooldridge, and the J-PAL Handbook of Field Experiments.1 The most up-to-date (free!) book on Machine Learning and causal inference is this book by Susan Athey’s Social Impact Lab at Stanford.

Other research resources

I highly recommend the following online resources.