nt-williams/crumble
General targeted machine learning for modern causal mediation analysis
When you need to understand how a treatment or intervention (like a new policy or drug) influences an outcome, not just directly, but also indirectly through other factors (mediators), crumble helps. You input your observational data, specify your treatment, outcome, and potential mediators, and it outputs a breakdown of different causal effects. Researchers, policymakers, and health professionals who design and evaluate interventions will find this useful for robust mediation analysis.
Use this if you need to quantify the direct and indirect causal pathways between an exposure and an outcome using advanced machine learning techniques, even with complex data.
Not ideal if you are looking for simple linear regression-based mediation analysis or if your data does not involve clearly defined treatments, mediators, and outcomes for causal inference.
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12
Forks
3
Language
R
License
GPL-3.0
Category
Last pushed
Feb 21, 2026
Commits (30d)
0
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