What I've Been Reading

Ironically, the need for a theory of causation began to surface at the same time that statistics came into being. In fact, modern statistics hatched from the causal questions that Galton and Pearson asked about heredity and their ingenious attempts to answer them using cross-generational data. Unfortunately, they failed in this endeavor, and rather than pause to ask why, they declared those questions off limits and turned to developing a thriving, causality-free enterprise called statistics.

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My emphasis on language also comes from a deep conviction that language shapes our thoughts. You cannot answer question that you cannot ask, and you cannot ask a question that you have no words for. 

The Book of Why: The New Science of Cause and Effect by Judea Pearl.

Wow ! one of the best books of the year. Easy ready even for a non-technical person.

1. My research on machine learning has taught me that a causal learner must master at least three distinct levels of cognitive ability: seeing, doing and imagining.
2. Bayes's rule informs our reasoning in cases where ordinary intuition fails us or where emotion might lead us astray.
3. Monty Hall Problem is a paradox because "They are accustomed to the reduction of data and ignoring the data-generating process (R.A Fisher, 1922).
4. To turn a noncausal Bayesian network into a causal model - or, more precisely, to make it capable of answering counterfactual queries - we need a dose-response relationship at each node.