#causalinference#causalmodels#statistics
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Intermediate. Assumes working knowledge.
743K views9 years ago1 recommendation
Computer scienceAI and machine learningComputation and complexityMath for machine learning
How causal models and networks allow us to mathematically infer causation from correlation.
Have fun improving your math & physics skills! Head to brilliant.org/minutephysics/ Footnote video: youtube.com/watch?v=iMbcMMe0D_Y This video is about how causal models (which use causal networks) allow us to infer causation from correlation, proving the common refrain not entirely accurate: statistics CAN be used to prove causality! Including: Reichenbach's principle, common causes, feedback, entanglement, EPR paradox, and so on. REFERENCES: Causal Discovery Algorithm in Quantum Mechanics Paper: arxiv.org/pdf/1208.4119.pdf Causal Models overview (Quantum and Classical): arxiv.org/pdf/1609.09487.pdf Support MinutePhysics on Patreon! patreon.com/minutephysics Link to Patreon Supporters: minutephysics.com/supporters/ MinutePhysics is on twitter - @minutephysics And facebook - facebook.com/minutephysics And Google+ (does anyone use this any more?) - bit.ly/qzEwc6 Minute Physics provides an energetic and entertaining view of old and new problems in physics -- all in a minute! Created by Henry ReichWhat people say. Recommended as a clear and concise explanation of how causal inference and causal networks can prove causality from statistical correlation.
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