Modeling and Discovering Direct Causes for Predictive Models

Authors

  • Yizuo Chen University of California, Los Angeles
  • Amit Bhatia RTX Technology Research Center

DOI:

https://doi.org/10.32473/flairs.38.1.139003

Keywords:

Causality, causal models, explainability

Abstract

We introduce a causal modeling framework that captures the input-output behavior of predictive models (e.g., machine learning models). The framework enables us to identify features that directly cause the predictions, which has broad implications for data collection and model evaluation. We then present sound and complete algorithms for discovering direct causes (from data) under some assumptions. Furthermore, we propose a novel independence rule that can be integrated with the algorithms to accelerate the discovery process as we demonstrate both theoretically and empirically.

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Published

14-05-2025

How to Cite

Chen, Y., & Bhatia, A. (2025). Modeling and Discovering Direct Causes for Predictive Models. The International FLAIRS Conference Proceedings, 38(1). https://doi.org/10.32473/flairs.38.1.139003

Issue

Section

Special Track: Uncertain Reasoning