Non-Technical Introduction to Causal Inference/Methods

Hi everyone, the blog is back in business.

I recently came across a great working paper seeking to introduce econometric methods geared towards understanding causality to a non-expert audience. I'm particularly excited about this because I think many of these methods could really be useful in medical care/clinical questions where it is either unethical or technically difficult to randomize patients (and yes, there are still plenty of those!). For whatever reason, these methods are, in my estimation, rather underutilized in medicine.

Obviously, while the linked piece is geared towards education policy, the methods can be used in any context. Here is the abstract:

Education policy-makers and practitioners want to know which policies and practices can best achieve their goals. But research that can inform evidence-based policy often requires complex methods to distinguish causation from accidental association. Avoiding econometric jargon and technical detail, this paper explains the main idea and intuition of leading empirical strategies devised to identify causal impacts and illustrates their use with real-world examples. It covers six evaluation methods: controlled experiments, lotteries of oversubscribed programs, instrumental variables, regression discontinuities, differences-in-differences, and panel-data techniques. Illustrating applications include evaluations of early-childhood interventions, voucher lotteries, funding programs for disadvantaged, and compulsory-school and tracking reforms.

Enjoy!