The Origins of Graphical Causal Models

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.pearl_causality_ch3

As you can see, the idea of thinking about interventions in quasi-deterministic systems is strongly rooted in econometrics (so no need for the not-invented-here-syndrome). In a sense, this story is also typical for the modern literature on machine learning, where smart computer scientists discover (some say “reinvent”, but that’s too disparaging for my taste) approaches from statistics and econometrics and take them to the next level. Because of his prior, Turing-award-worthy work on Bayesian networks, Pearl was able adapt the idea of structural causal models and equip it with a powerful symbolic language that allows us to solve problems far beyond what has been possible with traditional econometric techniques. Clearly, we have much to learn from each other and can only benefit from the convergence of interest from both disciplines.

If you want to know more about the history of graphical causal models and some of the amusing anecdotes around their origin (involving for example guinea pigs, but I shouldn’t spoiler), I can highly recommend you Pearl’s newest book “The Book of Why”, written together with Dana Mackenzie. It’s both an easily accessible introduction to the topic as well as an entertaining account of the last 25 years of Pearl’s research. On top of that you get some more funny rants about Karl Pearson—“causality’s worst adversary”. Definitely worth a read! :)

No Free Lunch in Causal Inference

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

Causality for Policy Assessment and Impact Analysis

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