Feature Matching Intervention: Leveraging Observational Data for Causal Representation Learning

• URL: http://arxiv.org/abs/2503.03634v1
• Date: Wed, 05 Mar 2025 16:14:43 GMT
• Title: Feature Matching Intervention: Leveraging Observational Data for Causal Representation Learning
• Authors: Haoze Li, Jun Xie,
• Abstract summary: Feature Matching Intervention (FMI) uses a matching procedure to mimic perfect interventions.<n>We define causal latent graphs, extending structural causal models to latent feature space.<n>Our feature matching procedure emulates perfect interventions within these causal latent graphs.
• Score: 25.30659475597803
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A major challenge in causal discovery from observational data is the absence of perfect interventions, making it difficult to distinguish causal features from spurious ones. We propose an innovative approach, Feature Matching Intervention (FMI), which uses a matching procedure to mimic perfect interventions. We define causal latent graphs, extending structural causal models to latent feature space, providing a framework that connects FMI with causal graph learning. Our feature matching procedure emulates perfect interventions within these causal latent graphs. Theoretical results demonstrate that FMI exhibits strong out-of-distribution (OOD) generalizability. Experiments further highlight FMI's superior performance in effectively identifying causal features solely from observational data.

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