Data science tutorial: causal inference model selection | Jonathan Hershaff
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Data science tutorial: causal inference model selection | Jonathan Hershaff
Like hyper-parameter tuning, model selection techniques are commonplace in machine learning but often overlooked in the causal inference space. I've sat in plenty of seminars with data scientists who had a time series and a limited set of treated units who chose a model and went with it without testing alternatives. This can have important ...
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Replying to @Crypex295 the first step in learning more about the causal inference track in data science is building a foundation of experimentation (a/b testing). Udacity offers a free course in this. From there, I’ve found nothing improves intuition better than testing techniques on simulated data #datascientist #dataanalyst #datascience #breakintotech #causalinference #datanalytics #breakintodatascience
Replying to @Crypex295 the first step in learning more about the ca…
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