Going beyond

The “causal inference revolution” means our course materials on program evaluation 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.

Doing the work

There is no better way of learning about evaluations than related internships or a related job once you graduate.

Of course, one way is to work with a professor at UCI or other university centers (such as the Annenberg Institute). I also highly recommend the The Abdul Latif Jameel Poverty Action Lab (J-PAL), where I started out myself. Without endorsing them, here is a long (and incomplete) list of other potential employers: Abt, AIR, Brookings, the Center for Global Development, CEPR (including the Strategic Data Project), development finance institutions (such as the AfDB, IADB, or the World Bank), IDinsight, the Institute of Education Sciences, IPA, Mathematica, MDRC, RAND, the Urban Institute, USAID, and WestEd. Finally, even if you want to work outside of education, causal inference skills are in very high demand in the economy, where many employers “are drowning in information but starving for wisdom.”

Other courses

  1. Andrew Heiss teaches a phenomenal program evaluation class 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.

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 and program evaluation, including this one by Imbens and Rubin, this “classic” by Jeff Wooldridge, and the J-PAL Handbook of Field Experiments.1 But stop here for a moment and consider just how much we’ve learned this quarter—I find it amazing how much we’ve already covered in our course!

Going beyond program evaluations, I also recommend the following books on social science research and writing.

Other online resources

I highly recommend the following online resources.