# Causal Abstraction in Model Interpretability: A Compact Survey

arXiv: https://arxiv.org/abs/2410.20161 · HTML: https://mlsanity.com/paper/2410.20161/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 26 references that are in the map.
- First posted on arXiv 26 Oct 2024.

## Builds on (most cited first, 20 of 20)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness?](https://mlsanity.com/paper/2004.03685/) (2020)
- [Causal Abstractions of Neural Networks](https://mlsanity.com/paper/2106.02997/) (2021)
- [A Tutorial on Principal Component Analysis](https://mlsanity.com/paper/1404.1100/) (2014)
- [Causal Consistency of Structural Equation Models](https://mlsanity.com/paper/1707.00819/) (2017)
- [Abstracting Causal Models](https://mlsanity.com/paper/1812.03789/) (2018)
- [Interpretability at Scale: Identifying Causal Mechanisms in Alpaca](https://mlsanity.com/paper/2305.08809/) (2023)
- [A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation](https://mlsanity.com/paper/2305.11391/) (2023)
- [Polysemanticity and Capacity in Neural Networks](https://mlsanity.com/paper/2210.01892/) (2022)
- [SelfIE: Self-Interpretation of Large Language Model Embeddings](https://mlsanity.com/paper/2403.10949/) (2024)
- [Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples](https://mlsanity.com/paper/1711.09576/) (2017)
- [Approximate Causal Abstraction](https://mlsanity.com/paper/1906.11583/) (2019)
- [Causal Inference by String Diagram Surgery](https://mlsanity.com/paper/1811.08338/) (2018)
- [Causal Abstraction with Soft Interventions](https://mlsanity.com/paper/2211.12270/) (2022)
- [Abstraction between Structural Causal Models: A Review of Definitions and Properties](https://mlsanity.com/paper/2207.08603/) (2022)
- [A Probabilistic Calculus of Actions](https://mlsanity.com/paper/1302.6835/) (2013)
- [On the Equivalence of Causal Models: A Category-Theoretic Approach](https://mlsanity.com/paper/2201.06981/) (2022)
- [Weighted Automata Extraction and Explanation of Recurrent Neural Networks for Natural Language Tasks](https://mlsanity.com/paper/2306.14040/) (2023)
- [Automata Extraction from Transformers](https://mlsanity.com/paper/2406.05564/) (2024)

## Built on by (most cited first, 2 of 2)

- [Ablate and Rescue: A Causal Analysis of Residual Stream Hyper-Connections](https://mlsanity.com/paper/2603.14833/) (2026)
- [Position: Explainable AI is Causality in Disguise](https://mlsanity.com/paper/2603.28597/) (2026)

## Abstract (arXiv)

The pursuit of interpretable artificial intelligence has led to significant advancements in the development of methods that aim to explain the decision-making processes of complex models, such as deep learning systems. Among these methods, causal abstraction stands out as a theoretical framework that provides a principled approach to understanding and explaining the causal mechanisms underlying model behavior. This survey paper delves into the realm of causal abstraction, examining its theoretical foundations, practical applications, and implications for the field of model interpretability.