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. ( )

"Let me explain why my solution is brillant and why statisticians are ignorant. I'll start back 200 years ago. Ho and with the remaining 10% of my book, let me share examples of actually using Causality diagrams. You know just in case you really want to use them."

Was expecting a complete teaching on how to approach causality with concrete tools and method for my daily work. Went out with a full history of fruitless dispute between the author and statisticians, between statisticians and statisticians and between the author and people on his own side.
Still, will try to use the approach using tutorials on the net. ( )

Ho preso questo libro perché un mio amico me l'ha caldamente suggerito; e questo mio amico è un pezzo grosso della ricerca, non un banale compagno di chiacchierate. Però non mi è piaciuto affatto. Sono perfettamente d'accordo sulla tesi di fondo, che cioè dobbiamo affrancarci da un approccio statistico a-causale, fatto puramente di correlazioni. Possiamo filosoficamente chiederci se effettivamente esista la causalità: ma visto che noi esseri umani agiamo pensando che ci sia tanto vale usarla anche nei nostri modelli. Però mi sono trovato un libro dove per buona parte Pearl scrive di come lui è bravissimo e praticamente da solo ha rovesciato il paradigma secolare della statistica; la parte più tecnica (quando parla di confonditori e mediatori, e fa i diagrammi con le frecce) è mischiata in modo tale che almeno io non sono riuscito a studiarla. Insomma, non mi sono portato a casa nulla. Ah: non c'entra con il libro, ma c'è stato un momento in cui il paperback costava cinque euro meno dell'ebook: poi il prezzo è stato allineato. Uno si chiede come mai ci siano queste variazioni... ( )

This book is not a casual one. It is packed full of path diagrams with formulas attached to them. The author tries to explain as much as he can about a few concepts of statistics that you need to know in order to understand what he's talking about. It's mainly aimed at scientists and engineers/programmers that need to have a good grasp of causality, correlation, inference and other statistical methods used to model real world problems. I enjoyed the book, but I dont think I truly understood more than 2/3. I will probably get back to it at some later point if I ever have to deal with causal diagrams. It stretched my understanding of statistical mathemathics and there were multiple points in the book where I had to give it my all to grasp what it was saying. Good read for exercising our mental reasoning models. ( )

So Dr. Pearl won the Turing Prize, which I think means he convinced a computer that he's human or something. The first part of the book is fascinating and informative. The author was involved directly in a lot of the history and it shows. Sometimes it almost shows too much, as in the author is almost bragging at times, but he does it in a way that doesn't really get annoying.

The second part of the book where he goes into great detail about causal diagrams and their manipulations is cool, but not entertaining reading for me. I skipped around a lot in the last part of the book. Still worth the read, at least the first part. ( )