Philosophy and Data Science — Thinking Deeply about Data | by Jarom Hulet | Jan, 2024

Part 3: Causality

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My hope is that by the end of this article you will have a good understanding of how philosophical thinking around causation applies to your work as a data scientist. Ideally you will have a deeper philosophical perspective to give context to your work!

This is the third part in a multi-part series about philosophy and data science. Part 1 covers how the theory of determinism connects with data science and part 2 is about how the philosophical field of epistemology can help you think critically as a data scientist.


I love how many philosophical topics take a seemingly obvious concept, like causality, and make you realize it is not as simple as you think. For example, without looking up a definition, try to define causality off the top of your head. That is a difficult task — for me at least! This exercise hopefully nudged you to realize that causality isn’t as black and white as you may have thought.

Here is what this article will cover:

  1. Challenges of observing causality
  2. Deterministic vs probabilistic causality
  3. Regularity theory of causality
  4. Process theory of causality
  5. Counterfactual theory of causality
  6. Bringing it all together

Causality’s Unobservability

David Hume, a famous skeptic and one of my favorite philosophers, made the astute observation that we cannot observe causality directly with our senses. Here’s a classic example: we can see a baseball flying towards the window and we can see the window break, but we cannot see the causality directly. We cannot…

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