# A Unified View of Causal and Non-causal Feature Selection

arXiv: https://arxiv.org/abs/1802.05844 · HTML: https://mlsanity.com/paper/1802.05844/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Feb 2018.

## Built on by (most cited first, 23 of 23)

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- [A Review of the Role of Causality in Developing Trustworthy AI Systems](https://mlsanity.com/paper/2302.06975/) (2023)
- [Structural Hawkes Processes for Learning Causal Structure from Discrete-Time Event Sequences](https://mlsanity.com/paper/2305.05986/) (2023)
- [DRCFS: Doubly Robust Causal Feature Selection](https://mlsanity.com/paper/2306.07024/) (2023)
- [Local Discovery by Partitioning: Polynomial-Time Causal Discovery Around Exposure-Outcome Pairs](https://mlsanity.com/paper/2310.17816/) (2023)
- [Towards Efficient Local Causal Structure Learning](https://mlsanity.com/paper/2103.00378/) (2021)
- [Local Causal Discovery for Structural Evidence of Direct Discrimination](https://mlsanity.com/paper/2405.14848/) (2024)
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- [Causally-Guided Automated Feature Engineering with Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2602.16435/) (2026)
- [Fair Causal Feature Selection](https://mlsanity.com/paper/2306.10336/) (2023)
- [Causal Multi-Label Feature Selection in Federated Setting](https://mlsanity.com/paper/2403.06419/) (2024)
- [An Embedded Feature Selection Framework for Control](https://mlsanity.com/paper/2206.11064/) (2022)
- [Explanatory causal effects for model agnostic explanations](https://mlsanity.com/paper/2206.11529/) (2022)
- [Causally-Aware Unsupervised Feature Selection Learning](https://mlsanity.com/paper/2410.12224/) (2024)
- [Any Part of Bayesian Network Structure Learning](https://mlsanity.com/paper/2103.13810/) (2021)
- [A-SFS: Semi-supervised Feature Selection based on Multi-task Self-supervision](https://mlsanity.com/paper/2207.09061/) (2022)
- [Linking a predictive model to causal effect estimation](https://mlsanity.com/paper/2304.04566/) (2023)
- [Nonnegative matrix factorization and the principle of the common cause](https://mlsanity.com/paper/2509.03652/) (2025)
- [Neural Autoregressive Flows for Markov Boundary Learning](https://mlsanity.com/paper/2603.20791/) (2026)
- [Do Real-World Datasets Contain Natural Experiments? An Empirical Study Using Causal Feature Selection](https://mlsanity.com/paper/2606.03251/) (2026)

## Abstract (arXiv)

In this paper, we aim to develop a unified view of causal and non-causal feature selection methods. The unified view will fill in the gap in the research of the relation between the two types of methods. Based on the Bayesian network framework and information theory, we first show that causal and non-causal feature selection methods share the same objective. That is to find the Markov blanket of a class attribute, the theoretically optimal feature set for classification. We then examine the assumptions made by causal and non-causal feature selection methods when searching for the optimal feature set, and unify the assumptions by mapping them to the restrictions on the structure of the Bayesian network model of the studied problem. We further analyze in detail how the structural assumptions lead to the different levels of approximations employed by the methods in their search, which then result in the approximations in the feature sets found by the methods with respect to the optimal feature set. With the unified view, we are able to interpret the output of non-causal methods from a causal perspective and derive the error bounds of both types of methods. Finally, we present practical understanding of the relation between causal and non-causal methods using extensive experiments with synthetic data and various types of real-word data.