Exploring structural causal models using DAGs, counterfactual reasoning, and frameworks like DoWhy and PyMC-Marketing
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Learn how Mixed Integer Programming (MIP) with discretized response curves solves marketing budget allocation problems using branch-and-bound methods.
A practical tutorial implementing causal marketing mix modeling with DoWhy, including code examples and a complete toy dataset
Walk through real-world MMM optimization scenarios using Mixed Integer Programming. See how to discretize response curves, formulate constraints, and solve practical reallocation problems with actual numbers.