Half 3: Causality
My hope is that by the tip of this text you should have a superb understanding of how philosophical pondering round causation applies to your work as an information scientist. Ideally you should have a deeper philosophical perspective to provide context to your work!
That is the third half in a multi-part sequence about philosophy and information science. Half 1 covers how the speculation of determinism connects with information science and half 2 is about how the philosophical area of epistemology might help you assume critically as an information scientist.
Introduction
I really like what number of philosophical subjects take a seemingly apparent idea, like causality, and make you understand it’s not so simple as you assume. For instance, with out trying up a definition, attempt to outline causality off the highest of your head. That could be a troublesome activity — for me no less than! This train hopefully nudged you to understand that causality isn’t as black and white as you could have thought.
Here’s what this text will cowl:
- Challenges of observing causality
- Deterministic vs probabilistic causality
- Regularity principle of causality
- Course of principle of causality
- Counterfactual principle of causality
- Bringing all of it collectively
Causality’s Unobservability
David Hume, a well-known skeptic and one among my favourite philosophers, made the astute statement that we can not observe causality immediately with our senses. Right here’s a traditional instance: we will see a baseball flying in the direction of the window and we will see the window break, however we can not see the causality immediately. We can not…
