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Perhaps the biggest issue with current machine learning trends, is our flawed tendency to interpret or describe the patterns captured in models as causative rather than correlations of unknown veracity, accuracy or impact.“ - Kalev Leetaru
➥ "The Limits Of Learning By Correlation Rather Than Causation"
➨ No matter how hauntingly accurate their results, today’s deep learning algorithms are still at the end of the day nothing more than statistical inferencing engines.
➨ The ease with which modern machine learning pipelines can transform a pile of data into a predictive model without requiring an understanding of statistics or even programming has been a key driving force in its rapid expansion into industry. At the same time, it has eroded the distinction between correlation and causation as the new generation of data scientists building and deploying these models conflate their predictive prowess with explanatory power.
➨ One of the most basic tenets 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.