# Identifying Patient-Specific Root Causes of Disease

arXiv: https://arxiv.org/abs/2205.11627 · HTML: https://mlsanity.com/paper/2205.11627/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 40 references that are in the map.
- First posted on arXiv 23 May 2022.

## Builds on (most cited first, 7 of 7)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Causal Discovery from Heterogeneous/Nonstationary Data with Independent Changes](https://mlsanity.com/paper/1903.01672/) (2019)
- [Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy To Game](https://mlsanity.com/paper/2102.13647/) (2021)
- [Causal structure based root cause analysis of outliers](https://mlsanity.com/paper/1912.02724/) (2019)
- [Why did the distribution change?](https://mlsanity.com/paper/2102.13384/) (2021)
- [Direct Estimation of Differences in Causal Graphs](https://mlsanity.com/paper/1802.05631/) (2018)
- [Direct Learning with Guarantees of the Difference DAG Between Structural Equation Models](https://mlsanity.com/paper/1906.12024/) (2019)

## Built on by (most cited first, 8 of 8)

- [Identifiability of latent-variable and structural-equation models: from linear to nonlinear](https://mlsanity.com/paper/2302.02672/) (2023)
- [Identifying Patient-Specific Root Causes with the Heteroscedastic Noise Model](https://mlsanity.com/paper/2205.13085/) (2022)
- [Why Do Probabilistic Clinical Models Fail To Transport Between Sites?](https://mlsanity.com/paper/2311.04787/) (2023)
- [Counterfactual Formulation of Patient-Specific Root Causes of Disease](https://mlsanity.com/paper/2305.17574/) (2023)
- [Sample-Specific Root Causal Inference with Latent Variables](https://mlsanity.com/paper/2210.15340/) (2022)
- [Unsupervised Discovery of Clinical Disease Signatures Using Probabilistic Independence](https://mlsanity.com/paper/2402.05802/) (2024)
- [A tutorial on discovering and quantifying the effect of latent causal sources of multimodal EHR data](https://mlsanity.com/paper/2510.16026/) (2025)
- [Extracting Root-Causal Brain Activity Driving Psychopathology from Resting State fMRI](https://mlsanity.com/paper/2602.07233/) (2026)

## Abstract (arXiv)

Complex diseases are caused by a multitude of factors that may differ between patients. As a result, hypothesis tests comparing all patients to all healthy controls can detect many significant variables with inconsequential effect sizes. A few highly predictive root causes may nevertheless generate disease within each patient. In this paper, we define patient-specific root causes as variables subject to exogenous "shocks" which go on to perturb an otherwise healthy system and induce disease. In other words, the variables are associated with the exogenous errors of a structural equation model (SEM), and these errors predict a downstream diagnostic label. We quantify predictivity using sample-specific Shapley values. This derivation allows us to develop a fast algorithm called Root Causal Inference for identifying patient-specific root causes by extracting the error terms of a linear SEM and then computing the Shapley value associated with each error. Experiments highlight considerable improvements in accuracy because the method uncovers root causes that may have large effect sizes at the individual level but clinically insignificant effect sizes at the group level. An R implementation is available at github.com/ericstrobl/RCI.