Jethro's Braindump

Causality, part 1 - Bernhard Schölkopf - MLSS 2020, Tübingen - YouTube

source
https://www.youtube.com/watch?v=btmJtThWmhA&feature=youtu.be

Background and Motivation

Consider a dataset of temperature \(t\) vs altitude \(a\). We typically see that the larger the altitude, the lower the temperature. How do we know if this is a causal effect?

  1. Intervention: we can raise the city, and find that the temperature changes
  2. Hypothetical intervention: expect that \(t\) changes, since we can think of a physical mechanism \(p(t|a)\) that is independent of \(p(a)\). We expect that \(p(t|a)\) is invariant across different countries in similar climate zone.

A “structural” relation not only explains the observed data; it captures a structure connecting the variables.

An equation becomes structural by virtue of invariance to a domain of modifications.

Structural Causal Model

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