Hajime Takeda
Hajime is a data professional with five years of expertise in marketing, retail, and eCommerce, working in New York.
As a Data Analyst at Procter and Gamble and MIKI HOUSE Americas, Hajime has led data-driven strategy formulation and implemented technology initiatives such as e-commerce expansion, advertising optimization, and the identification of growth opportunities.
Sessions
Causal inference has traditionally been used in the field of marketing. Uplift modeling is one of the major techniques of customer analytics and it helps companies to identify customers with the highest marketing effect when targeted.
In recent years, algorithms combining causal inference and machine learning have been a hot topic. CausalML is a good Python package developed by Uber and it provides a suite of uplift modeling and causal inference methods.
In this talk, I will show the key concepts of causal inference with machine learning, their application in marketing science (Uplift modeling), their demonstration using CausalML, and practical tips.