Why the rule that correlation doesn't equal causation is incomplete
In short: You have likely been told that statistics can never prove cause and effect. This video challenges that common refrain by exploring how specific causal models allow us to actually infer causality from data.
While many believe a statistical link only shows two things happening together, the video explains that using causal networks allows for deeper inference. It explores complex concepts such as Reichenbach's principle, feedback loops, and common causes to demonstrate how statistics can indeed be used to prove causality.
The discussion bridges fundamental physics and logic, touching on topics like entanglement and the EPR paradox to provide a comprehensive view of how we understand connections in the universe.
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Sources
- Summary by Intellect · source: Correlation CAN Imply Causation! | Statistics Misconceptions
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