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As someone who does social science causal inference for a living, I have to say that I didn't really enjoy "The Book of Why". Full disclosure: I mostly practice
by notafraudster 8y ago
As someone who does social science causal inference for a living, I have to say that I didn't really enjoy "The Book of Why". Full disclosure: I mostly practice the Neyman-Rubin potential outcomes form of causal inference rather than the Pearl do-calculus / DAG ("directed acyclic graph") form of causal inference, but the two are in many cases equivalent.
The reason I didn't like the book is that I found it insufficiently rigorous to really engage with the "how" of doing causal inference, but excessively mathematical as a theoretical introduction to causality.
"Causality: A Primer" (also written by Pearl) is a very short book that I think does a good job of surfacing some of the same theoretical background while also explaining how to use Pearl's causality. If you exhaust that, I'd recommend moving to the full "Causality" book.
But otherwise I'd recommend actually looking into the counterfactual / potential outcomes view of causality. The set of questions it answers are about 80% overlapping (although both Pearl and POs have their own 20%), but I find the vocabulary a little more intuitive. Canonical books include Morgan and Winship "Counterfactuals and Causal Inference" or Imbens and Rubin "Causal Inference for Social Scientists".
As to the blog post, Pearl is correct that causality requires qualitative assumptions about design to justify assumptions required to do causal inference. In Pearl's work this is often motivated as qualitative knowledge informing the structure of the DAG before any estimation. But recent advances in causal discovery have actually rendered it possible to black box the structure of a DAG from data -- happy to provide citations if this is down the rabbit hole. By contrast, I agree with Gelman that Pearl is an irritating writer and that in "The Book of Why" he gives a sloppy intellectual history of causation.
- breuderink 8y ago> But recent advances in causal discovery have actually rendered it possible to black box the structure of a DAG from data -- happy to provide citations if this is down the rabbit hole. I would be very interested in these references.
- notafraudster 8y agoI should say this is not my wing of the world since in social science typically theory precedes estimation and there would be a strong disciplinary norm against "I have no idea what causes what". So I don't actually use this stuff. That being said, I have played with a few of the packages and read a few pieces on causal discovery. Jonas Peters et al. - Elements of Causal Inference is a textbook that covers a little bit of what they called "learning cause-effect models". For algorithms, check SGS (Spirtes-Glymour-Scheines) and PC (Peter Spirtes and Clark Glymour). I believe both these algorithms are implemented in R in the package `pcalg`. There's another R package on BioConductor that implements them too, but I'm far enough afield from biostats I don't remember the name or have any notes I can find. Some recent cites of note: Peters and Buhlmann - "Identifiability of Gaussian structural equation models" (2014), which led to Ghoshal and Honorio - "Learning linear structural equation models in polynomial time" (2018) who generalize the Peters/Buhlmann claim. Other authors to Google: Dominik Janzig; Joris Mooij; Patrik Hoyer -- all of these people write papers with the above people, so you should be able to map out the network. What the pieces all have in common is that they're trying to establish empirical differences in the joint distributions of X and Y between scenarios where X -> Y and where Y -> X. This is only possible in some cases. Hope this helps.
- mooneater 8y agoIs this directly related to learning the structure of PGMs? eg. https://arxiv.org/abs/1111.6925 https://arxiv.org/abs/1111.6925 and practical example at https://github.com/jmschrei/pomegranate/blob/master/tutorials/B_Model_Tutorial_4b_Bayesian_Network_Structure_Learning.ipynb https://github.com/jmschrei/pomegranate/blob/master/tutorial...
- sjg007 8y agoYes.
- srean 8y agoI think that is unfair. No single book, except for a tome will cover all the bases. On the other hand if one does write such a tome it sets the author up for a low brow dissmissal - the author is incapable of giving a brief description and is too technical...TLDR.