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Philosophy and Knowledge Science — Considering Deeply about Knowledge | by Jarom Hulet | Jan, 2024


Half 3: Causality

Towards Data Science

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My hope is that by the tip of this text you’ll have a very good understanding of how philosophical pondering round causation applies to your work as a knowledge scientist. Ideally you’ll have a deeper philosophical perspective to offer context to your work!

That is the third half in a multi-part sequence about philosophy and information science. Half 1 covers how the idea of determinism connects with information science and half 2 is about how the philosophical subject of epistemology might help you suppose critically as a knowledge scientist.

Introduction

I like what number of philosophical matters take a seemingly apparent idea, like causality, and make you understand it isn’t so simple as you suppose. For instance, with out trying up a definition, attempt to outline causality off the highest of your head. That could be a tough process — for me a minimum of! This train hopefully nudged you to understand that causality isn’t as black and white as you might have thought.

Here’s what this text will cowl:

  1. Challenges of observing causality
  2. Deterministic vs probabilistic causality
  3. Regularity concept of causality
  4. Course of concept of causality
  5. Counterfactual concept of causality
  6. Bringing all of it collectively

Causality’s Unobservability

David Hume, a well-known skeptic and considered one of my favourite philosophers, made the astute statement that we can not observe causality immediately with our senses. Right here’s a basic instance: we are able to see a baseball flying in the direction of the window and we are able to see the window break, however we can not see the causality immediately. We can not…



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