Search Results for author:
Found 4 papers, 0 papers with code
Causal Discovery from Poisson Branching Structural Causal Model Using High-Order Cumulant with Path Analysis
Fortunately, in this work, we found that the causal order from $X$ to its child $Y$ is identifiable if $X$ is a root vertex and has at least two directed paths to $Y$, or the ancestor of $X$ with the most directed path to $X$ has a directed path to $Y$ without passing $X$.
When and How: Learning Identifiable Latent States for Nonstationary Time Series Forecasting
To solve this problem, we propose to learn IDentifiable latEnt stAtes (IDEA) to detect when the distribution shifts occur.
Identification of Causal Structure in the Presence of Missing Data with Additive Noise Model
With this observation, we aim to investigate the identification problem of learning causal structure from missing data under an additive noise model with different missingness mechanisms, where the `no self-masking missingness' assumption can be eliminated appropriately.
Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables
To address this, we propose a Generalized Independent Noise (GIN) condition for linear non-Gaussian acyclic causal models that incorporate latent variables, which establishes the independence between a linear combination of certain measured variables and some other measured variables.
