Bayesian Incrementality & Geo-Lift Modeling

A production-ready causal inference project focused on measuring incrementality using Bayesian methods and geo-based experiments.

This repository demonstrates how to estimate true causal lift from marketing interventions using Bayesian synthetic control and geo-experimentation.

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🎯 Problem Statement

Standard attribution models often overstate marketing impact by confusing correlation with causation.

This project answers the real business question:

What would have happened if we had NOT run the campaign?

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🧠 Methodology

• Bayesian Synthetic Control
• Geo-based experiment design (test vs control regions)
• Counterfactual outcome estimation
• Posterior credible intervals for lift
• Incrementality (ATT / Causal Lift) estimation

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📊 Key Outputs

• Counterfactual vs observed time series
• Incremental lift curves with uncertainty
• Cumulative and marginal impact estimates
• Decision-ready summaries for stakeholders

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📁 Project Structure

bayesian-incrementality-geo-lift/
├── notebooks/        # Exploratory and methodological notebooks
├── src/causal/       # Core causal modeling logic
├── src/viz/          # Lift & impact visualizations
├── data/             # Simulated geo-level dataset

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🚀 Quick Start

pip install pymc arviz numpy pandas matplotlib

Run the causal model:

python src/causal/geo_experiment.py

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🧪 Notebooks

1. 01_synthetic_geo_data.ipynb

   • Simulate geo-level time series data
   • Define treatment and control regions

2. 02_bayesian_synthetic_control.ipynb

   • Fit Bayesian synthetic control model
   • Estimate counterfactual outcomes
   • Compute incremental lift

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⚠️ Data

The dataset provided is fully simulated and included for demonstration purposes only.

No proprietary or confidential data is used.

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🛠 Tech Stack

• PyMC
• ArviZ
• NumPy / pandas
• Matplotlib
• Bayesian causal inference

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📄 License

MIT License