Introducing the do-sampler for causal inference

The promise of Pearlian causal inference

Figure 1: The difference between the observed and interventional distributions, as shown by the two causal graphs.

The Do-Sampler: An example

The Do-Sampler: Why it works

The Usual Caveats

  1. For an overview of causal inference techniques, check out a tutorial by Emre and Amit: https://causalinference.gitlab.io/kdd-tutorial/
  2. Check the Book of Why for an introduction. Or dive into the Causality book if you are brave enough!
  3. These logical rules are based on the do-calculus by Judea Pearl.
  4. To be precise, we condition on the author variable here. Controlling refers to intervening so that an author write articles of different lengths as in a randomized experiment.
  5. The source for the do-sampler is available at https://www.github.com/Microsoft/dowhy.

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Physicist; formerly Data @ BuzzFeed; Adjunct Prof. at Columbia;

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adam kelleher

adam kelleher

Physicist; formerly Data @ BuzzFeed; Adjunct Prof. at Columbia;

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