Testing Conditional Independence Between Latent Variables by Independence Residuals
Conditional independence (CI) testing is an important problem, especially in causal
discovery. Most testing methods assume that all variables are fully observable and then test …
discovery. Most testing methods assume that all variables are fully observable and then test …
Causal Structure Recovery with Latent Variables under Milder Distributional and Graphical Assumptions
Traditional causal discovery approaches typically assume the absence of latent variables, a
simplification that often does not align with real-world situations. Recently, there has been a …
simplification that often does not align with real-world situations. Recently, there has been a …
Learning Discrete Latent Variable Structures with Tensor Rank Conditions
Unobserved discrete data are ubiquitous in many scientific disciplines, and how to learn the
causal structure of these latent variables is crucial for uncovering data patterns. Most studies …
causal structure of these latent variables is crucial for uncovering data patterns. Most studies …