juangamella/icp
Python implementation of the Invariant Causal Prediction (ICP) algorithm, from the 2015 paper "Causal inference using invariant prediction: identification and confidence intervals" by Jonas Peters, Peter Bühlmann and Nicolai Meinshausen.
This tool helps researchers and data scientists analyze observational and experimental data to discover true cause-and-effect relationships, even when experiments are limited. You provide datasets from different experimental conditions, and it identifies which variables are direct causes of a target outcome. This is ideal for anyone trying to move beyond correlation to understand underlying causal drivers.
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Use this if you have multiple datasets from different experimental or observational settings and want to robustly identify the invariant causal predictors of a specific outcome.
Not ideal if you only have a single dataset and no information about different experimental or environmental conditions, as it relies on varying environments to find invariant causal relationships.
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Language
Python
License
BSD-3-Clause
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Last pushed
Feb 15, 2024
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