Guido Imbens published a new working paper in which he develops a detailed comparison of the potential outcomes framework (PO) and directed acyclic graphs (DAG) for causal inference in econometrics. I really appreciate this paper, because it introduces a broader audience in economics to DAGs and highlights the complementarity of both approaches for applied econometric work.
I have a couple of comments on specific points in the paper though, which I wrote down in several Twitter threads throughout the last weeks. I chose Twitter, because we had many, quite extensive, discussions about DAGs there in recent months (Guido even cites some of our tweets in his paper) and because many economists seem to be active on this platform these days. Tansferring all these threads into blog posts would – frankly – require too much time. But for archiving purposes, I will link to the start tweets of the individual threads here.
(I have also saved the full threads as text files in my documents. So if you don’t like going through them on Twitter, or maybe they will be deleted one day, you can always shoot me a meassge and I will send them to you.)
One argument / point of criticism I often hear from people who start exploring Directed Acyclic Graphs (DAG) is that graphical models can quickly become very complex. When you read about the methodology for the first time you get walked through all these toy models – small, well-behaved examples with nice properties, in which causal inference works like a charm.
Continue reading Graphs and Occam’s Razor
Instrumental variable (IV) estimation is an important technique in causal inference and applied empirical work. Continue reading You Can’t Test Instrument Validity
Here is an interesting bit of intellectual history. In his 2000 book “Causality”, Judea Pearl describes how he got to the initial idea that sparked the development of causal inference based on directed acyclic graphs. Continue reading The Origins of Graphical Causal Models
Last week I was teaching about graphical models of causation at a summer school in Montenegro. You can find my slides and accompanying R code in the teaching section of this page. It was lots of fun and I got great feedback from students. After the workshop we had stimulating discussions about the usefulness of this new approach to causal inference in economics and business. I’d like to pick up one of those points here, as this is an argument I frequently hear when talking to people with a classical econometrics training. Continue reading No Free Lunch in Causal Inference
Here is a great introductory lecture into causal inference and the power of directed acyclic graphs / bayesian networks. It repeats a point I made earlier on this blog that big data alone, without a causal model (i.e., theory) to support it, is simply not sufficient for making causal claims. Continue reading Causality for Policy Assessment and Impact Analysis