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:
- Challenges of observing causality
- Deterministic vs probabilistic causality
- Regularity concept of causality
- Course of concept of causality
- Counterfactual concept of causality
- 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…