Separate cause from correlation in your data.
What it does
Estimates intervention effects from observational data while accounting for confounding.
When to use it
Use to evaluate policies or business decisions, estimate treatment effects without randomized experiments, or investigate effects across user segments.
How to use it
Give it observational data to analyze; it returns treatment-effect estimates, diagnostics, sensitivity analysis, and a causal inference report.
What you provide
- Your files
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What you need · 7
The implementation uses Python; no version is specified.
The implementation imports pandas for tabular data processing.
The implementation imports numpy for numerical operations.
The implementation imports matplotlib.pyplot for visualizations.
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