Most causal discovery procedures assume that there are no latent confounders in the
system, which is often violated in real-world problems. In this paper, we consider a …

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 …

The goal of causal representation learning is to find a representation of data that consists of
causally related latent variables. We consider a setup where one has access to data from …

Identifying latent variables and causal structures from observational data is essential to
many real-world applications involving biological data, medical data, and unstructured data …

Most existing causal discovery methods rely on the assumption of no latent confounders,
limiting their applicability in solving real-life problems. In this paper, we introduce a novel …

We study the problem of learning hierarchical causal structure among latent variables from
measured variables. While some existing methods are able to recover the latent hierarchical …

The problem of causal discovery is especially challenging when the variables of interest
cannot be directly measured. In measurement models, the measured variables were …

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 …

Answering counterfactual queries has important applications such as explainability,
robustness, and fairness but is challenging when the causal variables are unobserved and …

Discovering causal structures with latent variables from observational data is a fundamental
challenge in causal discovery. Existing methods often rely on constraint-based, iterative …