Technical Ebook

Using Causal Inference

to Unpack the Why and What If

In an MIT article, computer science researcher Jeannette Wing says that “Causality … is the next frontier of AI and machine learning.” This technical ebook unpacks a hearty introduction to causal inference and all its idiosyncrasies, as well as the danger of using regular ML to infer causal effects.

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How Does Causal ML Differ From Traditional ML?

This Technical Ebook Highlights:

  • The foundational distinction between correlation and causation
  • A common application of causal inference, uplift modeling, in the lens of churn prediction
  • Three key aspects that highlight how traditional and causal ML differ along with the main assumptions made on data for causal ML (i.e., conditional ignorability, common support, and no interference)