This post delves into how Causal AI can enhance Marketing Mix Modelling (MMM) by incorporating causal reasoning to address complexities in marketing interactions. Key topics include the basics of MMM, regression, ad stock, saturation, and the use of causal graphs to improve marketing insights. Various experimental methods and
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Causal AI, exploring the integration of causal reasoning into machine learningValidating the Causal Impact of the Synthetic Control Methodcausal_ai/notebooks/enhancing marketing mix modelling with causal ai.ipynb at main ·…RegressionMMM Example Notebook - pymc-marketing 0.6.0 documentationAd stockweibull_adstock - pymc-marketing 0.6.0 documentationSaturationmichaelis_menten - pymc-marketing 0.6.0 documentationCausal graphsUsing Causal Graphs to answer causal questionsUnderstanding the marketing graphMaking Causal Discovery work in real-world business settingsConversion lift testsGeo lift testsValidating the Causal Impact of the Synthetic Control MethodSwitch back testingLift Test Calibration - pymc-marketing 0.6.0 documentationSort: