Mesoscopic Causal Discovery: Learning Target-Centric Causal Subgraphs via Differentiable Optimization

Causal discovery methods have traditionally focused on either recovering global causal structures or identifying local relationships around a target variable. However, many real-world applications require understanding causal mechanisms at an intermediate scale, where the goal is to uncover a structured subset of variables that are causally relevant to a target through both direct and multi-step pathways, without incurring the cost of full graph reconstruction. In this paper, we study Mesoscopic Causal Discovery in the form of target-centric k-hop causal subgraph recovery, bridging global and local causal learning. We formalize this setting as Target-Centric Causal Subgraph Discovery, which aims to recover a directed acyclic target-centric subgraph within a specified k-hop structural scope, capturing both immediate dependencies and the intermediate pathways through which causal influence propagates. This problem is challenging because extending beyond immediate neighborhoods rapidly enlarges the candidate space, while indirect causal pathways can be difficult to distinguish from spurious associations. To address these challenges, we propose Target-Conditioned Causal Subgraph Learning (TCSL), a differentiable framework that constructs a target-dependent candidate space from observational data and adaptively refines variable relevance through stochastic node gating while jointly learning causal structure and conditional distributions. TCSL combines conditional-flow modeling with a differentiable acyclicity constraint to ensure valid causal structures and extracts the target-centric k-hop causal subgraph from the learned structure without access to ground-truth neighborhood information. Extensive experiments on both synthetic and real-world datasets demonstrate the effectiveness of TCSL.