Steve writes:
Marc,
I have followed your blog silently for a while now and always appreciate your approach to applied econometrics. I have a question for you if you will indulge me. I am a fan of Peter Kennedy ever since his “Sinning in the Basement” article and its inclusion in his Guide to Econometrics. Peter’s chapter on applied economics has shaped my econometric and data science teaching daily ever since. Last week I discovered Judea Pearl’s Book of Why. So I set out to see what I had been missing. I discovered Pearl wrote a paper on Haavelmo who I read at Ohio State over 40 years ago. I discovered exchanges between Pearl and Guido Imbens … I began to search for Peter Kennedy and Judea Pearl. Your blog entry … is one of the few google hits that came up for that search.
As you mention you are a fan of both Kennedy and Pearl, here are the questions that are on my mind: Are there links between Kennedy’s guidance on how to do applied economics and Pearl’s how to do causality? Do the two together make one a better applied econometrician? Do the two together constitute something business cares about?
I wrote a piece on what Kennedy’s rules mean from an ethical point of view. It is going to be published by O’Reilly Media in a collection: 97 Things About Ethics Everyone In Data Science Should Know, edited by Bill Franks. You can find my entry at https://econdatascience.com/ethics-rules-in-applied-econometrics-and-data-science/
My answers:
Are there links between Kennedy’s guidance on how to do applied economics and Pearl’s how to do causality?
A little bit, but not that many. Kennedy was largely concerned with ethics (nowadays, he’d write about replicability, transparency, pre-analysis plans, and so on) and he was writing pre-Credibility Revolution (so before causality was “a thing”), whereas Pearl is largely concerned with making explicit the assumptions that can lead to causal inference, and he is clearly a major actor of the Credibility Revolution–if not in economics, at least clearly outside of it. I think the link is mainly this: Kennedy would say that you need to be clear about the limitations of your work; Pearl would say that you need to be clear about your assumptions, and about stating explicitly the causal model you have in mind. I see limitations as related to those assumptions.
Do the two together make one a better applied econometrician?
Yes, no doubt. Kennedy is all about observational data and about providing clear, intuitive, math- and jargon-free (as much as possible) guidance about how to do good empirical work, but ignoring causality. Pearl is all about causality. As an economist, I’d recommend Kennedy as well Angrist and Pischke more than I’d recommend Kennedy and Pearl, or I’d substitute Morgan and Winship for Pearl, since their book is more easily accessible to economists. (Note: This is not a knock on Judea Pearl, whose contribution to our understanding has been nothing short of colossal. I just find that his Causality book is overkill for most economists.)
Do the two together constitute something business cares about?
It depends on what allows maximizing profit—prediction or inference. If it’s prediction, then no: they should be all about machine learning methods. If it’s about causal inference, then yes, because the Kennedy and Pearl together will make you a better user of data and econometric methods, and a sharper thinker on identification and (structural) assumptions. You might think “Does business really do causal inference?” I know economists who work for Amazon, and it turns out the answer is “Yes,” at least for Amazon, which apparently has a number of people working on causal inference. (Though from a microeconomic-theoretic perspective, I imagine the ability to invest in that type of research is facilitated by extra-normal profits, as with any old R&D activity!)