Resources for Causal Inference

In the last 15-20 years or so, statisticians and econometricians have made great strides in thinking about how to establish causality in the absence of a clean randomized experiment. In the economics literature, the bar as been raised quite high as far backing up causal statements, and the same sort of rigor is now developing in other disciplines such as medicine and public health, political science, and sociology. The increasing availability of technically accessible resources for those who are interested in applying cutting-edge statistical techniques to address causal questions, while making minimal assumptions on things that cannot be observed, should hasten the convergence in methodological rigor across these disciplines.

A good example of a rigorous, but still applied and down-to-earth, treatment of causal inference is Counterfactuals and Causal Inference: Methods and Principles for Social Research by Morgan and Winship. I am currently working through this book and have found it fantastic. The book begins by tackling the problem of causal inference using both the algebraic potential outcomes model as well more intuitive graph theoretic approaches. After building this foundation, the book goes through different approaches to establish causality, offering clear expositions on the utility of these estimators given certain data structures and, most importantly, the kinds of assumptions that are needed to justify making causal statements with each of the methods. Methods covered in the book include matching, IV, panel data approaches such as fixed-effects and difference-in-differences, and partial identification/bounding. I think any serious applied researcher, regardless of primary discipline, should have these methods in his/her toolkit. This book offers excellent introductions into each and will put said researcher on his/her way.

In a monograph explicitly designed to have broad appeal, it is to be expected that some important points are underdeveloped or glossed over, entirely. For example, the section on weak instruments in IV estimation left a lot to be desired. However, the authors cite all of the important theoretical and empirical literature up to 2006: if you want to learn more, it's not hard to find out where you need to go.

Some other (more advanced) resources that I have found interesting and useful:

1) Identification for Prediction and Decision, by Charles Manski: Manksi has a different take on causal inference than most. Rather than try to find point estimates of a causal parameter, Manksi begins with very weak assumptions about the underlying social process and studies the extent to which one can create treatment effect bounds with these assumptions. As he notes in the introduction, some people find this to be unnecessarily conservative. However, in many situations, perhaps it is best to recognize that explicit causal inference may not be possible and/or may require very strong assumptions about things we cannot observe. It is in these situations where the utility of bounding is most apparent. Manksi makes a concerted effort to tie in his methodology with efforts to recover policy relevant parameters that, as the title suggests, help motivate prediction and decision. Here is a health care application (looking at Swan Ganz catherization) of the Manksi's approach - good stuff.

2) NBER Summer Course on Econometrics: This site supposedly has some 18 hours worth of high-quality lecture video and slides on topics ranging from program evaluation and causal inference, to different estimators getting at causal inference, to panel data, to GMM, etc. The talks are given by Guido Imbens and Jeff Wooldridge, both excellent econometricians. (The latter has written some phenomenal introductory and advanced econometrics textbooks.) I'm about halfway through the lecture series now and they are quite good. Most require prior knowledge of econometrics.

3) Handbook of Econometrics: James Heckman has will have some articles about program evaluation in the forthcoming version of the Handbook of Econometrics he is editing. As you'll see from the Morgan and Winship book, Heckman's name comes up a lot in the causal inference and identification literature. He's a superstar who has done some amazing work. I'll post a link once these resources become available.