Counterfactuals! There is currently a

*mishegas*at Andrew Gelman's place about Pearl and Mackenzie's*Book of Why*, which has a reference to this and has led me to the conclusion that I really need to find time to work my way through this entire book:**Cosma Shalizi**:*Advanced Data Analysis from an Elementary Point of View*: "The distributions we observe in the world are the outcome of complicated stochastic processes. The mechanisms which set the value of one variable inter-lock with those which set other variables. When we make a probabilistic prediction by conditioning—whether we predict**E**[Y|X=x] or Pr (Y|X=x) or something more complicated—we are just filtering the output of those mechanisms, picking out the cases where they happen to have set X to the value x, and looking at what goes along with that. When we make a causal prediction, we want to know what would happen if the usual mechanisms controlling X were suspended and it was set to x. How would this change propagate to the other variables? What distribution would result for Y? This is often, perhaps even usually, what people really want to know from a data analysis, and they settle for statistical prediction either because they think it is causal prediction, or for lack of a better alternative...**Jag Bhalla**:*Judea Pearl's 'The Book of Why' Shakes Up Correlation vs. Causality**Uncle Judea, Melanin, Genetics, and Educational Attainment...*: Am I profoundly stupid, or is Uncle Judea's framework of causal confounders—colliders—mediators a huge advance, perhaps not in helping those of you who think carefully do non-stupid statistics, but in helping those of us who do not think carefully do non-stupid statistics, and in providing a royal road to teaching people how to do not-stupid statistics...*This point is absolutely cognitive science-statistics-philosophy of probability gold!*: Judea Pearl, Madelyn Glymour, and Nicholas P. Jewell (2016):*Causal Inference in Statistics: A Primer*(New York: John Wiley & Sons: 978119186847): "Inquisitive students may wonder why it is that dependencies associated with conditioning on a collider are so surprising to most people—as in, for example, the Monty Hall example. The reason is that humans tend to associate dependence with causation...**Lisa R. Goldberg**:*Review of The Book of Why: The New Science of Cause and Effect*: "The graphical approach to causal inference that Pearl favors has been influential, but it is not the only approach.... The Neyman (or Neyman–Rubin) potential outcomes model... James Heckman, whose concept of 'fixing' resembles, superficially at least, the do operator that Pearl uses. Those who enjoy scholarly disputes may look to Andrew Gelman’s blog... or to the tributes written by Pearl and Heckman to the reclusive Nobel Laureate, Trygve Haavelmo, who pioneered causal inference in economics in 1940...

### Counterfactuals!