rca-causal-inference
Mathematical foundations of root cause analysis using Judea Pearl's Structural Causal Models (SCMs), do-calculus, counterfactual reasoning, Bayesian networks for fault diagnosis, graph-theoretic centrality for fault localization, information-theoretic methods (transfer entropy, mutual information), and Granger causality. Use when the incident involves quantifiable observational data, when you need to distinguish correlation from causation, when you want to compute "what would have happened if we had done X instead," or when building automated fault-detection systems that reason about cause rat
npx skills add majiayu000/claude-skill-registry --skill rca-causal-inference --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Causal-Inference RCA Most root cause analysis is qualitative — you interview people, draw diagrams, write narratives. That's necessary but insufficient for systems where you have rich observational data and need defensible, reproducible, quantitative causal claims. This skill teaches the mathematical techniques developed by Pearl, Spirtes, Glymour, and others to reason formally about cause from data: when you can identify a causal effect from observations alone, when you need an intervention, and how to compute what would have happened under a counterfactual. ## The Ladder of Causation Judea Pearl (*The Book of Why*, 2018) defines three levels of causal reasoning, each requiring strictly more machinery than the last: | Rung | Activity | Example question | Required | |---|---|---|---| | 1 | **Association** | "How often does latency correlate with CPU?" | Joint distribution P(X, Y) | | 2 | **Intervention** | "What happens to latency if we force CPU=50%?" | do-calculus, do(X) operator | | 3 | **Counterfactual** | "Given what happened, would latency have stayed normal if CPU had been 50%?" | Structural causal model + observed data | Most RCA lives implicitly at rung 3 — every "the ro
- The Ladder of Causation
- Structural Causal Models (SCMs)
- Why the DAG matters
- Backdoor criterion
- Frontdoor criterion
- Counterfactual computation
- Bayesian Networks for fault diagnosis
- Fault-diagnosis workflow
- Empirical performance
- Structure learning
- Graph-theoretic fault localization
- Centrality measures
- Paper 3 finding (our research)
- Information-theoretic methods
What does the rca-causal-inference skill do?
Mathematical foundations of root cause analysis using Judea Pearl's Structural Causal Models (SCMs), do-calculus, counterfactual reasoning, Bayesian networks for fault diagnosis, graph-theoretic centrality for fault localization, information-theoretic methods (transfer entropy, mutual information), and Granger causality. Use when the incident involves quantifiable observational data, when you need to distinguish correlation from causation, when you want to compute "what would have happened if we had done X instead," or when building automated fault-detection systems that reason about cause rat
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill rca-causal-inference --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.