msuzen/looper
A resource list for causality in statistics, data science and physics
This project provides a curated list of resources, code snippets, and small software solutions to help practitioners understand and apply causal analysis. It takes complex concepts from statistics, data science, and physics (including econometrics and epidemiology) and presents them in an accessible way. Researchers, data scientists, and anyone needing to discern cause-and-effect relationships from data would find this useful.
268 stars.
Use this if you need to learn about or implement causal inference techniques in your research or data analysis to move beyond simple correlations.
Not ideal if you are looking for a fully-fledged, production-ready software library for large-scale causal analysis.
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268
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31
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Last pushed
Jan 28, 2026
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