rca-causal-inference

name	rca-causal-inference
description	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 rather than pattern-match on symptoms.
type	skill
category	rca
status	stable
origin	tibsfox
modified	false
first_seen	"2026-04-11T00:00:00.000Z"
first_path	examples/skills/rca/rca-causal-inference/SKILL.md
superseded_by	null

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 root cause was X" statement is a counterfactual — but most RCA tools can only support rung 1. The gap is where spurious root causes come from.

Structural Causal Models (SCMs)

An SCM is a triple (U, V, F) where:

• U — exogenous variables (unobserved background conditions, modeled as independent random variables)
• V — endogenous variables (observed system variables)
• F — a set of structural equations, one per endogenous variable, expressing how each variable is generated from its parents and from exogenous noise

Example for a simple latency incident:

U = {u_traffic, u_cpu_noise, u_lat_noise}
V = {Traffic, CPU, Latency}
F: Traffic := u_traffic
   CPU     := 0.7 * Traffic + u_cpu_noise
   Latency := 0.3 * CPU + 0.1 * Traffic + u_lat_noise

This SCM encodes three claims: Traffic is exogenous, CPU is caused by Traffic plus noise, and Latency is caused by both CPU (primary) and Traffic (direct effect, e.g., queueing). A directed acyclic graph (DAG) corresponds one-to-one:

Traffic → CPU → Latency
       ↓________↑