I find stats talk around causality wanting: most of the time, the concept is simply side-stepped. To remedy this, I'll read through @yudapearl's seminal work 'Causality' using his 'Book of Why' as an interpretative key. In this thread, I'll share what I learn. Bayesian Networks and Probability Distributions. How can we model a joint probability distribution in a computer? What is the probabilistic connection? d-separation and testable implications from graphical methods, Ladder of Causation..

Blogpost:
david-salazar.github.io/2020/07/18/cau…
#rstats

Going over @rlmcelreath's latest book in #rstats. However, I am using #tidyverse tools. So far, I have watched the lectures, completed homework for week 1 and the exercises in chapter 2 of the book. Hope to make it till the end!
Btw, the book is amazing.
david-salazar.github.io/2020/04/19/sta… Week 2 of Statistical Rethinking! Lectures and homework completed. @rlmcelreath is equal parts entertaining and knowledgeable. Homework made crystal clear how difficult it is to set priors for polynomial models #tidyverse #rstats

david-salazar.github.io/2020/04/28/sta…

Trying to get my head around fat-tails by studying @nntaleb's latest technical book. Replicated some plots in #rstats #tidyverse
david-salazar.github.io/2020/04/17/fat… #rstats and @nntaleb's work. By fattening the tails, one learns that the tail events are convex to the scale of the distribution. Thus, the problem compunds: tail events have an increasingly large role, but we cannot estimate their probabilities reliably

david-salazar.github.io/2020/05/09/wha…