Feature Matching Intervention: Leveraging Observational Data for Causal Representation Learning

Haoze Li, Jun Xie

Keywords: Causal representation learning, Observational data, Out-of-distribution generalization

TL;DR: We propose a novel method, Feature Matching Intervention (FMI), which uses a matching procedure to mimic perfect interventions

Abstract: A major challenge in causal inference 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.

Primary Area: causal reasoning

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Submission Number: 5109