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Found 7 papers, 0 papers with code
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.
HateDebias: On the Diversity and Variability of Hate Speech Debiasing
To further meet the variability (i. e., the changing of bias attributes in datasets), we reorganize datasets to follow the continuous learning setting.
Automating the Selection of Proxy Variables of Unmeasured Confounders
In this paper, we investigate the estimation of causal effects among multiple treatments and a single outcome, all of which are affected by unmeasured confounders, within a linear causal model, without prior knowledge of the validity of proxy variables.
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 this end, 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.
