Analyze causal questions using do-calculus and DAG/SCM frameworks.
npx skills add https://github.com/K-Dense-AI/mimeographs --skill judea-pearl-k-dense-ai
Please help me install this Agent Skill. Skill: judea-pearl Source: https://github.com/K-Dense-AI/mimeographs/tree/main/mimeographs/judea-pearl Command: npx skills add https://github.com/K-Dense-AI/mimeographs --skill judea-pearl-k-dense-ai
Judea Pearl's causal reasoning framework guides AI to move beyond correlation, enabling explicit causal modeling, intervention reasoning, and counterfactual thinking.
Ask a causal question (e.g., "What would happen if we changed X?") and request a causal graph plus do-calculus steps to derive the outcome.
High-intent search queries and answers about installing and using this skill.
To distinguish causation from correlation, you must build an explicit causal model using DAGs and apply the Ladder of Causation. This framework evaluates AI capabilities to move beyond standard probability and identify true causal effects.
To use do-calculus for estimating causal effects, formulate an interventional query and request a causal graph. The framework applies the back-door criterion and do-calculus steps to derive identifiable outcomes and guide experimental design.
Counterfactual reasoning works by evaluating hypothetical scenarios using Structural Causal Models. This enables personalized decision making and covariate selection by determining what would happen under different interventions.
Yes, causal inference requires explicit causal models like Directed Acyclic Graphs (DAGs) or Structural Causal Models (SCM). You must provide the causal graph to apply do-calculus and generate interpretable guidance.
You should not use do-calculus when your causal effect is not identifiable from observational data, or when you lack an explicit causal model. It requires well-defined DAGs to apply the back-door criterion and transportability.
The Ladder of Causation is a framework categorizing questions as associational, interventional, or counterfactual. You need it for causal analysis to tailor reasoning accordingly and evaluate AI capabilities beyond basic probability.
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