# MACFE: A Meta-learning and Causality Based Feature Engineering Framework

arXiv: https://arxiv.org/abs/2207.04010 · HTML: https://mlsanity.com/paper/2207.04010/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 36 references that are in the map.
- First posted on arXiv 8 Jul 2022.

## Builds on (most cited first, 7 of 7)

- [OpenML: networked science in machine learning](https://mlsanity.com/paper/1407.7722/) (2014)
- [Feature Engineering for Predictive Modeling using Reinforcement Learning](https://mlsanity.com/paper/1709.07150/) (2017)
- [The autofeat Python Library for Automated Feature Engineering and Selection](https://mlsanity.com/paper/1901.07329/) (2019)
- [An Empirical Analysis of Feature Engineering for Predictive Modeling](https://mlsanity.com/paper/1701.07852/) (2017)
- [Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range](https://mlsanity.com/paper/1407.8038/) (2014)
- [Causality-based Feature Selection: Methods and Evaluations](https://mlsanity.com/paper/1911.07147/) (2019)
- [Characterizing classification datasets: a study of meta-features for meta-learning](https://mlsanity.com/paper/1808.10406/) (2018)

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

- [Automated data processing and feature engineering for deep learning and big data applications: a survey](https://mlsanity.com/paper/2403.11395/) (2024)
- [How Usable is Automated Feature Engineering for Tabular Data?](https://mlsanity.com/paper/2508.13932/) (2025)

## Abstract (arXiv)

Feature engineering has become one of the most important steps to improve model prediction performance, and to produce quality datasets. However, this process requires non-trivial domain-knowledge which involves a time-consuming process. Thereby, automating such process has become an active area of research and of interest in industrial applications. In this paper, a novel method, called Meta-learning and Causality Based Feature Engineering (MACFE), is proposed; our method is based on the use of meta-learning, feature distribution encoding, and causality feature selection. In MACFE, meta-learning is used to find the best transformations, then the search is accelerated by pre-selecting "original" features given their causal relevance. Experimental evaluations on popular classification datasets show that MACFE can improve the prediction performance across eight classifiers, outperforms the current state-of-the-art methods in average by at least 6.54%, and obtains an improvement of 2.71% over the best previous works.