BiomedSciAI/causallib
A Python package for modular causal inference analysis and model evaluations
This project helps researchers and analysts determine the true impact of an intervention, like a new drug or marketing campaign, from existing real-world observations rather than controlled experiments. You input observational data, including features, treatment assignments, and outcomes, and it outputs an estimate of the causal effect. It's designed for data scientists, biostatisticians, and market researchers who need to understand 'why' something happened.
810 stars. No commits in the last 6 months. Available on PyPI.
Use this if you need to estimate the causal effect of a treatment or intervention on an outcome using real-world, non-experimental data.
Not ideal if you are looking for automated confounder selection or if your primary need is to build predictive models without inferring causality.
Stars
810
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108
Language
Python
License
Apache-2.0
Category
Last pushed
Apr 06, 2025
Commits (30d)
0
Dependencies
8
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