Causal interpretability for machine learning-problems, methods and evaluation
Open accessAbstract
Machine learning models have had discernible achievements in a myriad of applications. However, most of these models are black-boxes, and it is obscure how the decisions are made by them. This makes the models unreliable and untrustworthy. To provide insights into the decision making processes of these models, a variety of traditional interpretable models have been proposed. Moreover, to generate more humanfriendly explanations, recent work on interpretability tries to answer questions related to causality such as "Why does this model makes such decisions?" or "Was it a specific feature that caused the decision made by the model?". In this work, models that aim to answer causal questions are referred to as causal interpretable models. The existing surveys have covered concepts and methodologies of traditional interpretability. In this work, we present a comprehensive survey on causal interpretable models from the aspects of the problems and methods. In addition, this survey provides in-depth insights into the existing evaluation metrics for measuring interpretability, which can help practitioners understand for what scenarios each evaluation metric is suitable.
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Model explanation and interpretability may not fully capture complex interactions in the model, leading to limited interpretability.
“May not fully capture interpretability complex interactions in the model, leading to limited”
From AI Fairness in Data Management and Analytics: A Review on Challenges, Methodologies and Applications (2023) · cites this paper as [269]
Model explanation and interpretability facilitate understanding of model behavior and potential biases.
“Facilitates understanding of model behavior and potential biases”
From AI Fairness in Data Management and Analytics: A Review on Challenges, Methodologies and Applications (2023) · cites this paper as [269]
Model explanation and interpretability provide insights into decision-making.
“Provides insights into decision-making”
From AI Fairness in Data Management and Analytics: A Review on Challenges, Methodologies and Applications (2023) · cites this paper as [269]
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- Authors
- Moraffah, R., Karami, M., Guo, R., Raglin, A., & Liu, H.
- Affiliations
- Arizona State University (US)
- DEVCOM Army Research Laboratory (US)
- Venue
- ACM SIGKDD Explorations Newsletter
- Published
- 2020-05-13
- Volume
- 22
- Issue
- 1
- Pages
- 18-33
- ISSN
- 1931-0145 (ISSN-L)
- 1931-0153
- OpenAlex
- W3026235644
- Cited by
- 238
- References
- 81
- Citations by year
- 2026: 26 · 2025: 39 · 2024: 44 · 2023: 48 · 2022: 36 · 2021: 37 · 2020: 8
- FWCI
- 12.68
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- ACM SIGKDD Explorations Newsletter (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
- Keywords
- explainable machine learning, causal inference, model interpretability, black-box models, causal questions
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