Constraint-based causal discovery is widely used for learning causal structures, but heavy reliance on conditional independence (CI) testing makes it computationally expensive in high-dimensional settings. To mitigate this limitation, many divide-and-conquer frameworks have been proposed, but most assume causal sufficiency, i.e., no latent variables. In this paper, we show that divide-and-conquer strategies can be theoretically generalized beyond causal sufficiency to settings with latent variables. Specifically, we propose a recursive decomposition framework, termed DiCoLa, that enables divide-and-conquer causal discovery in the presence of latent variables. It recursively decomposes the global learning task into smaller subproblems and integrates their solutions through a principled reconstruction step to recover the global structure. We theoretically establish the soundness and completeness of the proposed framework. Extensive experiments on synthetic data demonstrate that our approach significantly improves computational efficiency across a range of causal discovery algorithms, while experiments on a real-world dataset further illustrate its practical effectiveness.

Segmentation of curvilinear structures such as vasculature and road networks is challenging due to relatively weak signals and complex geometry/topology.

In this section we provide a detailed proof for the correctness and completeness of the ICD algorithm. For easier referencing we describe ICD in Algorithm 1, and describe the ICD-Sep conditions. A set Zis a subset of ICD-Sep(A,B) given r {0,...,|O| 2}, if and only if 1. |Z|= r, 2. Z Z, there exists a PDS-path ΠB(A,Z) such that, (a) |ΠB(A,Z)| r and (b) every node on ΠB(A,Z) is in Z, and 3. Z Z, node Z is a possible ancestor of Aor B (not a necessary condition). Denote A,B a pair of nodes from O that are connected in G and disconnected in D, and such that Ais not an ancestor of B in D. If A B |[Z0] S, where Z0 O is a minimal separating set having size n+ 1, then there exists a subset Z O having the same size of n+ 1 such that that A B |Z S, and for every node Z Zthere exists a PDS-path ΠB(A,Z) in G, such that every node V on the PDS-path is also in Z. Proof. It was previously shown that a minimal separating set for Aand B, where Ais not an ancestor of B, is a subset of D-Sep(A,B) (Spirtes et al., 2000, page 134 and Theorem 6.2; Spirtes et al., 1999).

We develop a general duality between neural networks and compositional kernel Hilbert spaces. We introduce the notion of a computation skeleton, an acyclic graph that succinctly describes both a family of neural networks and a kernel space. Random neural networks are generated from a skeleton through node replication followed by sampling from a normal distribution to assign weights. The kernel space consists of functions that arise by compositions, averaging, and non-linear transformations governed by the skeleton's graph topology and activation functions. We prove that random networks induce representations which approximate the kernel space. In particular, it follows that random weight initialization often yields a favorable starting point for optimization despite the worst-case intractability of training neural networks.

We develop a general duality between neural networks and compositional kernel Hilbert spaces. We introduce the notion of a computation skeleton, an acyclic graph that succinctly describes both a family of neural networks and a kernel space. Random neural networks are generated from a skeleton through node replication followed by sampling from a normal distribution to assign weights. The kernel space consists of functions that arise by compositions, averaging, and non-linear transformations governed by the skeleton's graph topology and activation functions. We prove that random networks induce representations which approximate the kernel space. In particular, it follows that random weight initialization often yields a favorable starting point for optimization despite the worst-case intractability of training neural networks.

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However, the undirected setting is often insufficient: only a causal (i.e., directed) network provides

This paper concerns, more specifically, the inconsistency of separating sets used to remove dispensable edges, iteratively, based on conditional independence tests.