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Found 18 papers, 4 papers with code
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.
Latent Hierarchical Causal Structure Discovery with Rank Constraints
Most causal discovery procedures assume that there are no latent confounders in the system, which is often violated in real-world problems.
Action-Sufficient State Representation Learning for Control with Structural Constraints
Perceived signals in real-world scenarios are usually high-dimensional and noisy, and finding and using their representation that contains essential and sufficient information required by downstream decision-making tasks will help improve computational efficiency and generalization ability in the tasks.
FRITL: A Hybrid Method for Causal Discovery in the Presence of Latent Confounders
The first step of our method uses the FCI procedure, which allows confounders and is able to produce asymptotically correct results.
Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs
Despite its success in certain domains, most existing methods focus on causal relations between observed variables, while in many scenarios the observed ones may not be the underlying causal variables (e. g., image pixels), but are generated by latent causal variables or confounders that are causally related.
Domain Adaptation as a Problem of Inference on Graphical Models
Such a graphical model distinguishes between constant and varied modules of the distribution and specifies the properties of the changes across domains, which serves as prior knowledge of the changing modules for the purpose of deriving the posterior of the target variable $Y$ in the target domain.
Specific and Shared Causal Relation Modeling and Mechanism-Based Clustering
The learned SSCM gives the specific causal knowledge for each individual as well as the general trend over the population.
Triad Constraints for Learning Causal Structure of Latent Variables
In this paper, by properly leveraging the non-Gaussianity of the data, we propose to estimate the structure over latent variables with the so-called Triad constraints: we design a form of "pseudo-residual" from three variables, and show that when causal relations are linear and noise terms are non-Gaussian, the causal direction between the latent variables for the three observed variables is identifiable by checking a certain kind of independence relationship.
Identification of Effective Connectivity Subregions
These algorithms allow for identification of subregions of voxels driving the connectivity between regions of interest, recovering valuable anatomical and functional information that is lost when ROIs are aggregated.
Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models
In many scientific fields, such as economics and neuroscience, we are often faced with nonstationary time series, and concerned with both finding causal relations and forecasting the values of variables of interest, both of which are particularly challenging in such nonstationary environments.
Causal Discovery from Heterogeneous/Nonstationary Data with Independent Changes
In this paper, we develop a framework for causal discovery from such data, called Constraint-based causal Discovery from heterogeneous/NOnstationary Data (CD-NOD), to find causal skeleton and directions and estimate the properties of mechanism changes.
Diagnosis of Autism Spectrum Disorder by Causal Influence Strength Learned from Resting-State fMRI Data
A substantial body of researches use Pearson's correlation coefficients, mutual information, or partial correlation to investigate the differences in brain connectivities between ASD and typical controls from functional Magnetic Resonance Imaging (fMRI).
Causal Discovery in the Presence of Missing Data
When these data entries are not missing completely at random, the (conditional) independence relations in the observed data may be different from those in the complete data generated by the underlying causal process.
Causal Generative Domain Adaptation Networks
For this purpose, we first propose a flexible Generative Domain Adaptation Network (G-DAN) with specific latent variables to capture changes in the generating process of features across domains.
Causal Discovery in the Presence of Measurement Error: Identifiability Conditions
This problem has received much attention in multiple fields, but it is not clear to what extent the causal model for the measurement-error-free variables can be identified in the presence of measurement error with unknown variance.
Mixed Graphical Models for Causal Analysis of Multi-modal Variables
Graphical causal models are an important tool for knowledge discovery because they can represent both the causal relations between variables and the multivariate probability distributions over the data.
Discovery and Visualization of Nonstationary Causal Models
Third, we develop a method for visualizing the nonstationarity of causal modules.
Integrating Locally Learned Causal Structures with Overlapping Variables
In many domains, data are distributed among datasets that share only some variables; other recorded variables may occur in only one dataset.
