Judea Pearl

This is an engaging, well articulated discussion of causal inference - what it is, what the available tools are (RCTs, IVs, matching, etc), how they have changed over the years, and how they could be improved. The bits that tell the history of causal inference are especially illuminating; I learned a lot of stats in grad school but very little about the struggles and accidents that produced the tools I learned. Pearl helps put much of that into context.

Now, Pearl's intended audience is clearly the machine learning community. Much of what he says will not sound particularly Earth-shattering to people in (or from) the social sciences. "You can't learn causality from data alone, you need a model!" is one of the book's core messages. It's hard to see an economist or political scientist disagreeing with it. You come up with a theory, you think up its observable implications, you test them. Even Pearl's proposal that we use mediation analysis won't sound exactly novel. Social scientists have been doing that, they just don't use that name for it (they call it "testing the theory's microfoundations"). Now, having abandoned political science and lived among the machine learning people for four years now, I can see how Pearl's message is important to his intended audience. And social scientists should read the book too because it intelligently discusses the limitations of tools like RCTs and matching.

In the end what Pearl proposes - that we use our knowledge of how the world works in order to formulate and test hypotheses - may turn out to be (deservedly) influential in the machine learning community, but it won't help fix the core problem with the social sciences, i.e., that social scientists can always twist their hypotheses - not to mention the very questions they ask - to accomodate their pet world views. And when the Democrat/Republican ratio is 6:1, as it is in political science, we can't trust that people will keep each other honest - they won't. Pearl discusses in passing the possibility that some day we may have machine learning algorithms capable of producing their own causal models. Maybe then the social sciences will be worth the money they cost taxpayers.… (meer)