Most data scientists know that ‘association does not imply causation’. However, traditional data science and machine learning methods are about association, not causation. At the same time, causal questions are central to many data science problems across sectors, e.g. questions about measuring effects, drivers, incrementality, or about why a change in a certain KPI took place. In this session, we will show how the recently developed mathematical apparatus for causal inference (graphical causal models and do-calculus) enables data scientists to move from association to causation, and we’ll demonstrate the application of the causal data science pipeline on a retail sector problem using the DoWhy library in Python.

Please find all links to resources mentioned in the session here.


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