Applies Judea Pearl's causal reasoning frameworks to distinguish correlation from causation, evaluate AI capabilities, and make counterfactual decisions. Reach for this skill whenever Claude encounters questions about causal inference, structural causal models, the limitations of deep learning, AGI, experimental design, covariate selection, or personalized decision-making. Trigger this skill for topics involving Bayesian networks, the do-calculus, the Ladder of Causation, or when a user tries to answer 'what if' or 'why' questions using purely observational data. Pearl's principles are essential for moving beyond probability calculus into true causal understanding.
How to install judea-pearl?
k-dense-ai/mimeo/judea-pearl
$npx -y skills add k-dense-ai/mimeo --skill judea-pearlInstalls into the current project.
›Prefer a prompt? Paste this to your agent
Run `npx skills use "https://github.com/k-dense-ai/mimeo" --skill "k-dense-ai/mimeo/judea-pearl"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/k-dense-ai/mimeo" that are relevant to the current task. Run `npx skills add "https://github.com/k-dense-ai/mimeo"` and select the relevant skills, then follow their instructions.
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