My hope is that by the tip of this text you should have a superb understanding of how philosophical considering around causation applies to your work as a knowledge scientist. Ideally you should have a deeper philosophical perspective to provide context to your work!
That is the third part in a multi-part series about philosophy and data science. Part 1 covers how the speculation of determinism connects with data science and part 2 is about how the philosophical field of epistemology can assist you to think critically as a knowledge scientist.
Introduction
I really like what number of philosophical topics take a seemingly obvious concept, like causality, and make you understand it isn’t so simple as you think that. For instance, without looking up a definition, attempt to define causality off the highest of your head. That may be a difficult task — for me no less than! This exercise hopefully nudged you to understand that causality isn’t as black and white as you might have thought.
Here’s what this text will cover:
- Challenges of observing causality
- Deterministic vs probabilistic causality
- Regularity theory of causality
- Process theory of causality
- Counterfactual theory of causality
- Bringing all of it together
Causality’s Unobservability
David Hume, a famous skeptic and one in every of my favorite philosophers, made the astute remark that we cannot observe causality directly with our senses. Here’s a classic example: we will see a baseball flying towards the window and we will see the window break, but we cannot see the causality directly. We cannot…