Probability of Root Cause: A Counterfactual Definition and Its Identification

TL;DR

This paper introduces a formal, counterfactual-based definition of root cause and proposes the probability of root cause (PRC) to quantify its likelihood, with proven identifiability under standard assumptions.

Contribution

It provides a novel formal definition of root cause within the potential outcomes framework and derives an explicit formula for identifying the probability of root cause.

Findings

The PRC is identifiable under standard assumptions.

An explicit identification formula for PRC is derived.

Numerical examples demonstrate the approach.

Abstract

Attributing an observed outcome to its root cause is a central task in domains ranging from medical diagnosis to engineering fault diagnosis. Existing approaches either equate the root cause with a root node of the causal graph, as in causal-discovery-based root cause analysis, or target causes more broadly and thereby favour proximate ones, as with the probability of causation and posterior causal effects. We argue that this issue stems from the absence of a formal definition of a root cause, which has led to methods designed for other purposes being applied to root cause attribution by default. We address this by giving a formal, individual-level definition of a root cause within the potential outcomes framework, based on the notion of an individual cause and a counterfactual root condition motivated by mediation analysis. Building on this definition, we propose the probability of root cause (PRC), which quantifies how probable it is that a candidate variable set is the root cause of a given outcome, conditional on observed evidence. Under standard assumptions, we establish the identifiability of the PRC and derive an explicit identification formula. Two numerical examples illustrate the approach.