Multi-View Causal Discovery without Non-Gaussianity: Identifiability and Algorithms
This work addresses causal discovery for researchers in fields like neuroscience by enabling graph estimation with weaker assumptions, though it is incremental as it builds on existing single-view methods.
The paper tackles the problem of causal discovery by leveraging multi-view data to reduce reliance on strong assumptions like non-Gaussianity, proposing a multi-view linear SEM and proving its identifiability, with algorithms validated on simulations and neuroimaging data to estimate causal brain graphs.
Causal discovery is a difficult problem that typically relies on strong assumptions on the data-generating model, such as non-Gaussianity. In practice, many modern applications provide multiple related views of the same system, which has rarely been considered for causal discovery. Here, we leverage this multi-view structure to achieve causal discovery with weak assumptions. We propose a multi-view linear Structural Equation Model (SEM) that extends the well-known framework of non-Gaussian disturbances by alternatively leveraging correlation over views. We prove the identifiability of the model for acyclic SEMs. Subsequently, we propose several multi-view causal discovery algorithms, inspired by single-view algorithms (DirectLiNGAM, PairwiseLiNGAM, and ICA-LiNGAM). The new methods are validated through simulations and applications on neuroimaging data, where they enable the estimation of causal graphs between brain regions.