🤖 AI Summary
The absence of genuine interventions in observational data impedes reliable distinction between causal and spurious features. To address this, we propose Feature-Matching Intervention (FMI), a novel framework that constructs a causal latent graph in the representation space and simulates perfect intervention via mechanism matching—enabling intervention-free identification of causal features. Our key contributions are: (1) the first intervention paradigm grounded in feature matching; (2) formal definition of a causal latent graph to uniformly encode structural causal relationships in latent space; and (3) theoretical guarantees for strong out-of-distribution (OOD) generalization. FMI synergistically integrates causal representation learning, matching estimation, and latent-variable structural causal models (SCMs), operating solely on observational data. Empirically, it achieves significant improvements in causal feature identification accuracy across multiple OOD benchmarks, consistently outperforming state-of-the-art methods.
📝 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.