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What is Causal Machine Learning and Why Should You Care?

From Causal Machine Learning by Robert Ness

What is causal AI?

Why should I or my team care about causal data science and AI?

Better data science

Simulated experiments and causal effect inference

Counterfactual Data science

Better attribution, credit assignment, and root cause Analysis

More explainable Machine Learning

More Valuable Machine Learning

Figure 1. Causal models can be decomposed into components. This ability has benefits when contrasted with large machine learning artifacts.

Why I wrote this book

How is this book different from other causal inference books?

  • For common problems of causal inference, we can work with software tools that handle the probability theory and statistical theory for us. We can focus on learning to use those tools.
  • When we need to do more bespoke causal modeling, we can rely on generative machine learning tools. Those tools will help us handle the probability and statistical theory for us using blackbox inference techniques, including cutting edge deep learning-based methods like variational inference.
  1. Showing you how to turn domain knowledge into code representing testable causal assumptions.
  2. Showing you at a high level how those causal assumptions guide algorithmic causal inference.
  3. Working with machine learning software libraries that implement those inference algorithms.

Who Should Read This Book?

  • Data scientists, machine learning engineers, and code-savvy product managers looking to solve causal inference problems in industry with production-quality code.
  • Researchers who want to apply causal inference to their domain of expertise without having to get Ph.D.-level depth into statistical estimation theory and design of experiments.
  • Statisticians and economists who know a few causal inference methods and want a birds-eye view that ties it all together.
  • People who want to get in on the ground floor of causal AI.

What is the required mathematical and programming background?

  • Probability distributions.
  • Joint probability and conditional probability and how they relate together (chain rule, Bayes rule).
  • What it means to draw samples from a distribution.
  • Expectation, independence, and conditional independence.
  • Statistical ideas such as random samples, identically and independently sampled data, and statistical bias.

What programming tools do you use and what is the expected level of usage?



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