Difference between revisions of "Causation vs. Correlation"
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One of the most basic tenants of statistics is that correlation does not imply causation. In turn, a signal’s predictive power does not necessarily imply in any way that that signal is actually related to or explains the phenomena being predicted. This distinction matters when it comes to machine learning because many of the strongest signals these algorithms pick up in their training data are not actually related to the thing being measured. [http://www.forbes.com/sites/kalevleetaru/2019/01/15/a-reminder-that-machine-learning-is-about-correlations-not-causation/#2ca509766161 A Reminder That Machine Learning Is About Correlations Not Causation | Kalev Leetaru - Forbes] | One of the most basic tenants of statistics is that correlation does not imply causation. In turn, a signal’s predictive power does not necessarily imply in any way that that signal is actually related to or explains the phenomena being predicted. This distinction matters when it comes to machine learning because many of the strongest signals these algorithms pick up in their training data are not actually related to the thing being measured. [http://www.forbes.com/sites/kalevleetaru/2019/01/15/a-reminder-that-machine-learning-is-about-correlations-not-causation/#2ca509766161 A Reminder That Machine Learning Is About Correlations Not Causation | Kalev Leetaru - Forbes] | ||
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Revision as of 23:16, 21 June 2019
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One of the most basic tenants of statistics is that correlation does not imply causation. In turn, a signal’s predictive power does not necessarily imply in any way that that signal is actually related to or explains the phenomena being predicted. This distinction matters when it comes to machine learning because many of the strongest signals these algorithms pick up in their training data are not actually related to the thing being measured. A Reminder That Machine Learning Is About Correlations Not Causation | Kalev Leetaru - Forbes
Getting to Causality
