Data scientists are often asked causal questions, like "what would happen if we did X?", but are often can't run experiments to answer them. There's a mature literature on methods for causal inference, but it takes a big time investment to develop the skills to understand the methods and pitfalls. Ill argue for the need to invest this time, and give a quick overview of methods and tools, including my causality python package.
Adam spent more than 4 years at Buzzfee where he was a Principal Data Scientist. Adam holds a PhD in physics. He's now heading Barclays's research data science team, and teaching applied causal inference at Columbia's Data Science Institute where he is a Adjunct Assistant Professor.
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