b'Jiuyong Li'

research

∙ 04/24/2023

Causal Effect Estimation with Variational AutoEncoder and the Front Door Criterion

An essential problem in causal inference is estimating causal effects fr...

0 Ziqi Xu, et al. ∙

research

∙ 04/10/2023

Linking a predictive model to causal effect estimation

A predictive model makes outcome predictions based on some given feature...

0 Jiuyong Li, et al. ∙

research

∙ 02/19/2023

Disentangled Representation for Causal Mediation Analysis

Estimating direct and indirect causal effects from observational data is...

0 Ziqi Xu, et al. ∙

research

∙ 11/29/2022

Causal Inference with Conditional Instruments using Deep Generative Models

The instrumental variable (IV) approach is a widely used way to estimate...

0 Debo Cheng, et al. ∙

research

∙ 08/20/2022

Data-Driven Causal Effect Estimation Based on Graphical Causal Modelling: A Survey

In many fields of scientific research and real-world applications, unbia...

0 Debo Cheng, et al. ∙

research

∙ 08/19/2022

Disentangled Representation with Causal Constraints for Counterfactual Fairness

Much research has been devoted to the problem of learning fair represent...

0 Ziqi Xu, et al. ∙

research

∙ 06/23/2022

Explanatory causal effects for model agnostic explanations

This paper studies the problem of estimating the contributions of featur...

0 Jiuyong Li, et al. ∙

research

∙ 06/04/2022

Discovering Ancestral Instrumental Variables for Causal Inference from Observational Data

Instrumental variable (IV) is a powerful approach to inferring the causa...

0 Debo Cheng, et al. ∙

research

∙ 01/11/2022

Ancestral instrument method for causal inference without a causal graph

Unobserved confounding is the main obstacle to causal effect estimation ...

0 Debo Cheng, et al. ∙

research

∙ 12/20/2021

Feature Selection for Efficient Local-to-Global Bayesian Network Structure Learning

Local-to-global learning approach plays an essential role in Bayesian ne...

5 Kui Yu, et al. ∙

research

∙ 03/23/2021

Any Part of Bayesian Network Structure Learning

We study an interesting and challenging problem, learning any part of a ...

14 Zhaolong Ling, et al. ∙

research

∙ 03/11/2021

Causal Learner: A Toolbox for Causal Structure and Markov Blanket Learning

Causal Learner is a toolbox for learning causal structure and Markov bla...

0 Zhaolong Ling, et al. ∙

research

∙ 11/13/2020

Dependency-based Anomaly Detection: Framework, Methods and Benchmark

Anomaly detection is an important research problem because anomalies oft...

0 Sha Lu, et al. ∙

research

∙ 11/12/2020

Learning causal representations for robust domain adaptation

Domain adaptation solves the learning problem in a target domain by leve...

5 Shuai Yang, et al. ∙

research

∙ 10/08/2020

Assessing the Fairness of Classifiers with Collider Bias

The increasing maturity of machine learning technologies and their appli...

3 Zhenlong Xu, et al. ∙

research

∙ 09/14/2020

Sufficient Dimension Reduction for Average Causal Effect Estimation

Having a large number of covariates can have a negative impact on the qu...

9 Debo Cheng, et al. ∙

research

∙ 07/20/2020

Local search for efficient causal effect estimation

Causal effect estimation from observational data is an important but cha...

0 Debo Cheng, et al. ∙

research

∙ 07/14/2020

A unified survey on treatment effect heterogeneity modeling and uplift modeling

A central question in many fields of scientific research is to determine...

0 Weijia Zhang, et al. ∙

research

∙ 07/02/2020

Computational methods for cancer driver discovery: A survey

Motivation: Uncovering the genomic causes of cancer, known as cancer dri...

0 Vu Viet Hoang Pham, et al. ∙

research

∙ 03/25/2020

A general framework for causal classification

In many applications, there is a need to predict the effect of an interv...

15 Jiuyong Li, et al. ∙

research

∙ 02/24/2020

Towards precise causal effect estimation from data with hidden variables

Causal effect estimation from observational data is a crucial but challe...

18 Debo Cheng, et al. ∙

research

∙ 01/29/2020

Treatment effect estimation with disentangled latent factors

A pressing concern faced by cancer patients is their prognosis under dif...

16 Weijia Zhang, et al. ∙

research

∙ 01/28/2020

Causal query in observational data with hidden variables

This paper discusses the problem of causal query in observational data w...

7 Debo Cheng, et al. ∙

research

∙ 11/17/2019

Causality-based Feature Selection: Methods and Evaluations

Feature selection is a crucial preprocessing step in data analytics and ...

39 Kui Yu, et al. ∙

research

∙ 08/13/2019

Linking Graph Entities with Multiplicity and Provenance

Entity linking is a fundamental database problem with applicationsin dat...

0 Jixue Liu, et al. ∙

research

∙ 06/14/2019

Identify treatment effect patterns for personalised decisions

In personalised decision making, evidence is required to determine suita...

0 Jiuyong Li, et al. ∙

research

∙ 02/13/2019

Distributionally Robust Multi-instance Learning with Stable Instances

Multi-instance learning (MIL) deals with tasks where data consist of set...

0 Weijia Zhang, et al. ∙

research

∙ 11/06/2018

An exploration of algorithmic discrimination in data and classification

Algorithmic discrimination is an important aspect when data is used for ...

0 Jixue Liu, et al. ∙

research

∙ 11/05/2018

FairMod - Making Predictive Models Discrimination Aware

Predictive models such as decision trees and neural networks may produce...

0 Jixue Liu, et al. ∙

research

∙ 08/20/2018

Discovering Context Specific Causal Relationships

With the increasing need of personalised decision making, such as person...

0 Saisai Ma, et al. ∙

research

∙ 02/16/2018

A Unified View of Causal and Non-causal Feature Selection

In this paper, we unify causal and non-causal feature feature selection ...

0 Kui Yu, et al. ∙

research

∙ 01/25/2018

Discovering Markov Blanket from Multiple interventional Datasets

In this paper, we study the problem of discovering the Markov blanket (M...

0 Kui Yu, et al. ∙

research

∙ 11/12/2016

A Review on Algorithms for Constraint-based Causal Discovery

Causal discovery studies the problem of mining causal relationships betw...

0 Kui Yu, et al. ∙

research

∙ 10/11/2015

ParallelPC: an R package for efficient constraint based causal exploration

Discovering causal relationships from data is the ultimate goal of many ...

0 Thuc Duy Le, et al. ∙

research

∙ 08/28/2015

Mining Combined Causes in Large Data Sets

In recent years, many methods have been developed for detecting causal r...

0 Saisai Ma, et al. ∙

research

∙ 08/16/2015

From Observational Studies to Causal Rule Mining

Randomised controlled trials (RCTs) are the most effective approach to c...

0 Jiuyong Li, et al. ∙

research

∙ 08/16/2015

Causal Decision Trees

Uncovering causal relationships in data is a major objective of data ana...

0 Jiuyong Li, et al. ∙

Jiuyong Li

Featured Co-authors