# CIMLA: Interpretable AI for inference of differential causal networks

arXiv: https://arxiv.org/abs/2304.12523 · HTML: https://mlsanity.com/paper/2304.12523/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 89 references that are in the map.
- First posted on arXiv 25 Apr 2023.

## Builds on (most cited first, 13 of 13)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1704.02685/) (2017)
- [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://mlsanity.com/paper/1603.04467/) (2016)
- [Feature relevance quantification in explainable AI: A causal problem](https://mlsanity.com/paper/1910.13413/) (2019)
- [Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models](https://mlsanity.com/paper/2011.01625/) (2020)
- [Bayesian regression tree models for causal inference: regularization, confounding, and heterogeneous effects](https://mlsanity.com/paper/1706.09523/) (2017)
- [WeightedSHAP: analyzing and improving Shapley based feature attributions](https://mlsanity.com/paper/2209.13429/) (2022)
- [Unbiased Measurement of Feature Importance in Tree-Based Methods](https://mlsanity.com/paper/1903.05179/) (2019)
- [A Debiased MDI Feature Importance Measure for Random Forests](https://mlsanity.com/paper/1906.10845/) (2019)
- [Estimation of Causal Effects of Multiple Treatments in Observational Studies with a Binary Outcome](https://mlsanity.com/paper/2001.06483/) (2020)
- [Unbiased variable importance for random forests](https://mlsanity.com/paper/2003.02106/) (2020)
- [Individualized and Global Feature Attributions for Gradient Boosted Trees in the Presence of ℓ2 Regularization](https://mlsanity.com/paper/2211.04409/) (2022)

## Abstract (arXiv)

The discovery of causal relationships from high-dimensional data is a major open problem in bioinformatics. Machine learning and feature attribution models have shown great promise in this context but lack causal interpretation. Here, we show that a popular feature attribution model estimates a causal quantity reflecting the influence of one variable on another, under certain assumptions. We leverage this insight to implement a new tool, CIMLA, for discovering condition-dependent changes in causal relationships. We then use CIMLA to identify differences in gene regulatory networks between biological conditions, a problem that has received great attention in recent years. Using extensive benchmarking on simulated data sets, we show that CIMLA is more robust to confounding variables and is more accurate than leading methods. Finally, we employ CIMLA to analyze a previously published single-cell RNA-seq data set collected from subjects with and without Alzheimer's disease (AD), discovering several potential regulators of AD.