# Sample-Specific Root Causal Inference with Latent Variables

arXiv: https://arxiv.org/abs/2210.15340 · HTML: https://mlsanity.com/paper/2210.15340/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 33 references that are in the map.
- First posted on arXiv 27 Oct 2022.

## Builds on (most cited first, 10 of 10)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [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)
- [Identifying Patient-Specific Root Causes with the Heteroscedastic Noise Model](https://mlsanity.com/paper/2205.13085/) (2022)
- [Why did the distribution change?](https://mlsanity.com/paper/2102.13384/) (2021)
- [A Constraint-Based Algorithm For Causal Discovery with Cycles, Latent Variables and Selection Bias](https://mlsanity.com/paper/1805.02087/) (2018)
- [FASK with Interventional Knowledge Recovers Edges from the Sachs Model](https://mlsanity.com/paper/1805.03108/) (2018)
- [Identifying Patient-Specific Root Causes of Disease](https://mlsanity.com/paper/2205.11627/) (2022)
- [Counting Small Permutation Patterns](https://mlsanity.com/paper/1911.01414/) (2019)
- [Explaining the root causes of unit-level changes](https://mlsanity.com/paper/2206.12986/) (2022)

## Built on by (most cited first, 6 of 6)

- [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)
- [Unsupervised Discovery of Clinical Disease Signatures Using Probabilistic Independence](https://mlsanity.com/paper/2402.05802/) (2024)
- [An Identifiable Cost-Aware Causal Decision-Making Framework Using Counterfactual Reasoning](https://mlsanity.com/paper/2505.08343/) (2025)
- [Latent Variable Modeling for Robust Causal Effect Estimation](https://mlsanity.com/paper/2508.20259/) (2025)
- [Root Cause Analysis of Measurement and Mechanistic Anomalies](https://mlsanity.com/paper/2601.23026/) (2026)

## Abstract (arXiv)

Root causal analysis seeks to identify the set of initial perturbations that induce an unwanted outcome. In prior work, we defined sample-specific root causes of disease using exogenous error terms that predict a diagnosis in a structural equation model. We rigorously quantified predictivity using Shapley values. However, the associated algorithms for inferring root causes assume no latent confounding. We relax this assumption by permitting confounding among the predictors. We then introduce a corresponding procedure called Extract Errors with Latents (EEL) for recovering the error terms up to contamination by vertices on certain paths under the linear non-Gaussian acyclic model. EEL also identifies the smallest sets of dependent errors for fast computation of the Shapley values. The algorithm bypasses the hard problem of estimating the underlying causal graph in both cases. Experiments highlight the superior accuracy and robustness of EEL relative to its predecessors.