Causal Inference in Python: From Theory to Practice

Explore causal inference in Python through this 44-minute talk by Dr. Dimitra Liotsiou from dunhumby at the Data Science Festival. Delve into the distinction between association and causation, and discover how the recently developed mathematical apparatus for causal inference, including graphical causal models and do-calculus, empowers data scientists to address causal questions. Learn about the application of the causal data science pipeline to a retail sector problem using the DoWhy library in Python. Gain insights into measuring effects, drivers, incrementality, and understanding the reasons behind changes in key performance indicators. Access additional resources related to this talk on the Data Science Festival website to further enhance your understanding of causal inference techniques and their practical applications in data science.

Causal Inference in Python: Theory to Practice