Causal Feature Selection via Transfer Entropy
This work addresses the issue of misleading feature selection in real-world applications for machine learning practitioners by integrating causal discovery, though it appears incremental as it builds on existing feature selection and causal methods.
The paper tackles the problem of feature selection in high-dimensional time series data by introducing a causal feature selection method that uses transfer entropy to estimate causal information flow, providing theoretical guarantees and showing competitive results on synthetic and real-world regression problems.
Machine learning algorithms are designed to capture complex relationships between features. In this context, the high dimensionality of data often results in poor model performance, with the risk of overfitting. Feature selection, the process of selecting a subset of relevant and non-redundant features, is, therefore, an essential step to mitigate these issues. However, classical feature selection approaches do not inspect the causal relationship between selected features and target, which can lead to misleading results in real-world applications. Causal discovery, instead, aims to identify causal relationships between features with observational data. In this paper, we propose a novel methodology at the intersection between feature selection and causal discovery, focusing on time series. We introduce a new causal feature selection approach that relies on the forward and backward feature selection procedures and leverages transfer entropy to estimate the causal flow of information from the features to the target in time series. Our approach enables the selection of features not only in terms of mere model performance but also captures the causal information flow. In this context, we provide theoretical guarantees on the regression and classification errors for both the exact and the finite-sample cases. Finally, we present numerical validations on synthetic and real-world regression problems, showing results competitive w.r.t. the considered baselines.