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
I found this job ad by accident on Twitter and was surprised to see that Facebook has a causal inference group. Continue reading Facebook’s Causal Inference Group
In my class we recently discussed a paper by Higgins and Rodriguez (2006)—published in the Journal of Financial Economics—that contains an important lesson for researchers who want to apply the difference-in-differences (DiD) method in competition analysis and merger control. Continue reading Becoming More Different Over Time
This is a fair copy of a recent Twitter thread of mine. I thought it might be interesting to develop my arguments in a bit more detail and preserve them for later use.
[This post requires some knowledge of directed acyclic graphs (DAG) and causal inference. Providing an introduction to the topic goes beyond the scope of this blog though. But you can have a look at a recent paper of mine in which I describe this method in more detail.]
Graphical models of causation, most notably associated with the name of computer scientist Judea Pearl, received a lot of pushback from the grandees of econometrics. Heckman had his famous debate with Pearl, arguing that economics looks back on its own tradition of causal inference, going back to Haavelmo, and that we don’t need DAGs. Continue reading Econometrics and the “not invented here” syndrome: suggestive evidence from the causal graph literature
An interesting paper by Daniel Bradley, Incheol Kim, and Xuan Tian got recently published in Management Science (link to the SSRN version): Continue reading Labor unions may affect innovation negatively