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Causal inference in data science

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Amit Sharma
Amit Sharma

The document discusses using correlations to build predictive models and compares the success rates of two algorithms, Algorithm A and Algorithm B, on different user groups. It also notes that while average comment length decreases over time across all users, length increases over time for each yearly cohort. References are provided on causation and spurious correlations.

Data & Analytics
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1 http://www.amitsharma.in http://www.github.com/amit-sharma/causal-inference- tutorial
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Use these correlations to make a predictive model. Future Activity -> f(number of friends, logins in past month)  6
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19 Algorithm A Algorithm B 50/1000 (5%) 54/1000 (5.4%)
20 Algorithm A Algorithm B 10/400 (2.5%) 4/200 (2%) Algorithm A Algorithm B 40/600 (6.6%) 50/800 (6.2%)
Is Algorithm A better? Algorithm A Algorithm B Success Rate for Low-Activity users 10/400 (2.5%) 4/200 (2%) Success Rate for High-Activity users 40/600 (6.6%) 50/800 (6.2%) Total Success Rate 50/1000 (5%) 54/1000 (5.4%) 21
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Average comment length decreases over time. 23 But for each yearly cohort of users, comment length increases over time.
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27http://plato.stanford.edu/entries/causation-mani/
28http://plato.stanford.edu/entries/causation-counterfactual/
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38Dunning (2002), Rosenzweig-Wolpin (2000)
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47http://tylervigen.com/spurious-correlations
http://www.github.com/amit-sharma/causal-inference-tutorial amshar@microsoft.com 48

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