Search Results for author:
Found 24 papers, 7 papers with code
Causal Multi-Label Feature Selection in Federated Setting
Multi-label feature selection serves as an effective mean for dealing with high-dimensional multi-label data.
Causal Inference with Conditional Front-Door Adjustment and Identifiable Variational Autoencoder
In this paper, we relax some of the restrictions by proposing the concept of conditional front-door (CFD) adjustment and develop the theorem that guarantees the causal effect identifiability of CFD adjustment.
Fair Causal Feature Selection
Fair feature selection for classification decision tasks has recently garnered significant attention from researchers.
Causal Effect Estimation with Variational AutoEncoder and the Front Door Criterion
In practice, it is often difficult to identify the set of variables used for front-door adjustment from data.
Towards Privacy-Aware Causal Structure Learning in Federated Setting
In this paper, we study a privacy-aware causal structure learning problem in the federated setting and propose a novel Federated PC (FedPC) algorithm with two new strategies for preserving data privacy without centralizing data.
Explanatory causal effects for model agnostic explanations
This paper studies the problem of estimating the contributions of features to the prediction of a specific instance by a machine learning model and the overall contribution of a feature to the model.
Discovering Ancestral Instrumental Variables for Causal Inference from Observational Data
Based on the theory, we propose a data-driven algorithm to discover a pair of IVs from data.
Learning Relation-Specific Representations for Few-shot Knowledge Graph Completion
To this end, these methods learn entity-pair representations from the direct neighbors of head and tail entities, and then aggregate the representations of reference entity pairs.
Any Part of Bayesian Network Structure Learning
We study an interesting and challenging problem, learning any part of a Bayesian network (BN) structure.
Causal Learner: A Toolbox for Causal Structure and Markov Blanket Learning
Causal Learner is a toolbox for learning causal structure and Markov blanket (MB) from data.
A Local Similarity-Preserving Framework for Nonlinear Dimensionality Reduction with Neural Networks
Real-world data usually have high dimensionality and it is important to mitigate the curse of dimensionality.
Towards Efficient Local Causal Structure Learning
Local causal structure learning aims to discover and distinguish direct causes (parents) and direct effects (children) of a variable of interest from data.
Dependency-based Anomaly Detection: a General Framework and Comprehensive Evaluation
Anomaly detection is crucial for understanding unusual behaviors in data, as anomalies offer valuable insights.
Learning causal representations for robust domain adaptation
In this paper, we study the cases where at the training phase the target domain data is unavailable and only well-labeled source domain data is available, called robust domain adaptation.
A general framework for causal classification
We also propose a general framework for causal classification, by using off-the-shelf supervised methods for flexible implementations.
Towards unique and unbiased causal effect estimation from data with hidden variables
Based on the theorems, two algorithms are proposed for finding the proper adjustment sets from data with hidden variables to obtain unbiased and unique causal effect estimation.
Causal query in observational data with hidden variables
In this paper, we develop a theorem for using local search to find a superset of the adjustment (or confounding) variables for causal effect estimation from observational data under a realistic pretreatment assumption.
Causality-based Feature Selection: Methods and Evaluations
It has been shown that the knowledge about the causal relationships between features and the class variable has potential benefits for building interpretable and robust prediction models, since causal relationships imply the underlying mechanism of a system.
Towards Efficient Local Causal Structure Learning
To tackle this issue, we propose a novel Efficient Local Causal Structure learning algorithm, named ELCS.
A Unified View of Causal and Non-causal Feature Selection
The unified view will fill in the gap in the research of the relation between the two types of methods.
Discovering Markov Blanket from Multiple interventional Datasets
In this paper, we study the problem of discovering the Markov blanket (MB) of a target variable from multiple interventional datasets.
A Review on Algorithms for Constraint-based Causal Discovery
Recent years, as the availability of abundant large-sized and complex observational data, the constrain-based approaches have gradually attracted a lot of interest and have been widely applied to many diverse real-world problems due to the fast running speed and easy generalizing to the problem of causal insufficiency.
LOFS: Library of Online Streaming Feature Selection
As an emerging research direction, online streaming feature selection deals with sequentially added dimensions in a feature space while the number of data instances is fixed.
Scalable and Accurate Online Feature Selection for Big Data
Feature selection is important in many big data applications.
