​“Tiger got to hunt, bird got to fly;
Man got to sit and wonder 'why, why, why?'
Tiger got to sleep, bird got to land;
Man got to tell himself he understand.”
— Kurt Vonnegut

​Why do human beings care so much about "why" stuff happens?

​Is it important? Why is it important? (Uh-oh, there I go again!) If it isn't important, why are we so obsessed with it? (I can't help it, take pity)

​What does it mean for something to have a "why" anyway? Oftentimes we don't define what this means, or what it implies.

​But once we inspect it deeply, we unlock a universe of tools and techniques we can use to hunt down the cause-and-effect truths that intoxicate us.

​We'll explore the why and how of Why in three sessions and on Slack @ #cc-how-of-why:

​I - 🧠 The Call of Causality

​Feb 28th, 8 PM - 9 PM

​We want to know why stuff happens. In this session we'll break down what "Why" even means, with some lessons learned from sources like David Hume (an 18th century philosopher) and Judea Pearl (a Turing Award-winning scientist at UCLA).

​We might also gain some insight as to why this urge to seek causes and effects is so dang useful—and explain why it's such a deep need for us.

​Optional readings for Session 1.

​II - 🧪 The Era of the Experiment

​Mar 7th, 8 PM - 9 PM

​Armed with the clarity for what "Why" means, we can start seeking out answers.

​The most fool-proof way to do that is to break stuff and look inside—in other words, we can do an experiment. We'll be taking a look at how experimentation wasn't necessarily the way we've always sought truth, why it's so special, and where they can fail us.

​III - 👁️ Watch & Learn

​Mar 14th, 8 PM - 9 PM

​Experiments need you to do something and see what happens. But we learn how the world works by observation all the time—how in the hell does that work?

​Is there a formal way we do that? Can we teach computers to do it?

​In this session we'll graduate from experimentation to some basic econometrics. We'll do a quick refresher on some probability and statistics and take a look at ways we can learn cause-and-effect just with close observation—with some dope examples from recent literature.