reallift 0.1.1

Causal Inference Library for Lift Measurement

🚀 RealLift

Causal Inference Library for Lift Measurement

RealLift is a powerful Python library designed to help data scientists and marketers measure the true impact of their interventions (treatments) using advanced causal inference techniques, such as GeoLift, Synthetic Control, and Placebo Testing.

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✨ Features

• 🎯 Geo-Splitting: Automatically find the best treatment and control groups based on historical correlation.
• 📈 Synthetic Control: Build counterfactuals using a weighted combination of control regions.
• 🧪 Significance Testing: Robust bootstrap-based confidence intervals and p-values.
• 🛡️ Placebo Tests: Validate your model's reliability by running "fake" experiments on control groups.
• ⏳ Duration Estimation: Calculate the necessary experiment duration based on MDE (Minimum Detectable Effect).
• 📊 Professional Visualizations: Built-in plotting for experiment results and validation.

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🚀 Installation

Install the stable version via PyPI:

pip install reallift

Or install the latest development version directly from GitHub:

pip install git+https://github.com/RobertoJuniorWXYZ/RealLift.git

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⚡ Quick Start

1. Requirements & Design

Before starting an experiment, find the best clusters and estimate the required duration for a target MDE.

from reallift import run_geo_requirements

# Find best geo clusters and estimate duration
summary = run_geo_requirements(
    filepath="historical_data.csv",
    date_col="date",
    n_treatment=1,
    mde=0.015,
    max_days=60
)

2. Run a Complete Geo Experiment

Execute the full pipeline including validation, duration estimation, synthetic control, and placebo tests.

from reallift import run_geo_experiment

# Run full experiment pipeline
result = run_geo_experiment(
    filepath="your_data.csv",
    date_col="date",
    treatment_start_date="2025-05-01",
    n_treatment=1,
    mde=0.02
)

# Access results
print(f"Observed Lift: {result['results'][0]['synthetic']['lift_mean_abs']:.4f}")

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📖 Examples

For a deep dive into the library's capabilities, check out our demonstration notebooks in the examples/geotests/ directory.

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🛠️ Requirements

• Python 3.8+
• Pandas, Numpy, Scikit-Learn, Scipy, Matplotlib, CVXPY

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📄 License

MIT License. See LICENSE for details.

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Developed with ❤️ by Roberto Junior