# Causality-based Feature Selection: Methods and Evaluations

arXiv: https://arxiv.org/abs/1911.07147 · HTML: https://mlsanity.com/paper/1911.07147/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2019.

## Built on by (most cited first, 11 of 11)

- [Causal Reasoning Meets Visual Representation Learning: A Prospective Study](https://mlsanity.com/paper/2204.12037/) (2022)
- [A Review of the Role of Causality in Developing Trustworthy AI Systems](https://mlsanity.com/paper/2302.06975/) (2023)
- [Incorporating Causal Graphical Prior Knowledge into Predictive Modeling via Simple Data Augmentation](https://mlsanity.com/paper/2103.00136/) (2021)
- [Vision Paper: Causal Inference for Interpretable and Robust Machine Learning in Mobility Analysis](https://mlsanity.com/paper/2210.10010/) (2022)
- [Causal Learner: A Toolbox for Causal Structure and Markov Blanket Learning](https://mlsanity.com/paper/2103.06544/) (2021)
- [Selecting Robust Features for Machine Learning Applications using Multidata Causal Discovery](https://mlsanity.com/paper/2304.05294/) (2023)
- [Improving Data-driven Heterogeneous Treatment Effect Estimation Under Structure Uncertainty](https://mlsanity.com/paper/2206.12689/) (2022)
- [MACFE: A Meta-learning and Causality Based Feature Engineering Framework](https://mlsanity.com/paper/2207.04010/) (2022)
- [RuDi: Explaining Behavior Sequence Models by Automatic Statistics Generation and Rule Distillation](https://mlsanity.com/paper/2208.07211/) (2022)
- [Fast & Efficient Learning of Bayesian Networks from Data: Knowledge Discovery and Causality](https://mlsanity.com/paper/2310.09222/) (2023)
- [Knoop: Practical Enhancement of Knockoff with Over-Parameterization for Variable Selection](https://mlsanity.com/paper/2501.17889/) (2025)

## Abstract (arXiv)

Feature selection is a crucial preprocessing step in data analytics and machine learning. Classical feature selection algorithms select features based on the correlations between predictive features and the class variable and do not attempt to capture causal relationships between them. It has been shown that the knowledge about the causal relationships between features and the class variable has potential benefits for building interpretable and robust prediction models, since causal relationships imply the underlying mechanism of a system. Consequently, causality-based feature selection has gradually attracted greater attentions and many algorithms have been proposed. In this paper, we present a comprehensive review of recent advances in causality-based feature selection. To facilitate the development of new algorithms in the research area and make it easy for the comparisons between new methods and existing ones, we develop the first open-source package, called CausalFS, which consists of most of the representative causality-based feature selection algorithms (available at https://github.com/kuiy/CausalFS). Using CausalFS, we conduct extensive experiments to compare the representative algorithms with both synthetic and real-world data sets. Finally, we discuss some challenging problems to be tackled in future causality-based feature selection research.