Search Results for author: Jiuyong Li

Found 37 papers, 5 papers with code

Linking a predictive model to causal effect estimation

no code implementations10 Apr 2023 Jiuyong Li, Lin Liu, Ziqi Xu, Ha Xuan Tran, Thuc Duy Le, Jixue Liu

This paper first tackles the challenge of estimating the causal effect of any feature (as the treatment) on the outcome w. r. t.

Decision Making Fairness

Disentangled Representation for Causal Mediation Analysis

1 code implementation19 Feb 2023 Ziqi Xu, Debo Cheng, Jiuyong Li, Jixue Liu, Lin Liu, Ke Wang

Causal mediation analysis is a method that is often used to reveal direct and indirect effects.

Causal Inference with Conditional Instruments using Deep Generative Models

no code implementations29 Nov 2022 Debo Cheng, Ziqi Xu, Jiuyong Li, Lin Liu, Jixue Liu, Thuc Duy Le

The instrumental variable (IV) approach is a widely used way to estimate the causal effects of a treatment on an outcome of interest from observational data with latent confounders.

Causal Inference

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

no code implementations20 Aug 2022 Debo Cheng, Jiuyong Li, Lin Liu, Jixue Liu, Thuc Duy Le

In recent years, research has emerged to use a search strategy based on graphical causal modelling to discover useful knowledge from data for causal effect estimation, with some mild assumptions, and has shown promose in tackling the practical challenge.

Decision Making

Disentangled Representation with Causal Constraints for Counterfactual Fairness

no code implementations19 Aug 2022 Ziqi Xu, Jixue Liu, Debo Cheng, Jiuyong Li, Lin Liu, Ke Wang

Much research has been devoted to the problem of learning fair representations; however, they do not explicitly the relationship between latent representations.

Fairness Representation Learning

Explanatory causal effects for model agnostic explanations

no code implementations23 Jun 2022 Jiuyong Li, Ha Xuan Tran, Thuc Duy Le, Lin Liu, Kui Yu, Jixue Liu

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.

Ancestral instrument method for causal inference without a causal graph

no code implementations11 Jan 2022 Debo Cheng, Jiuyong Li, Lin Liu, Jiji Zhang, Thuc Duy Le, Jixue Liu

Conditional IV has been proposed to relax the requirement of standard IV by conditioning on a set of observed variables (known as a conditioning set for a conditional IV).

Causal Inference

Any Part of Bayesian Network Structure Learning

no code implementations23 Mar 2021 Zhaolong Ling, Kui Yu, Hao Wang, Lin Liu, Jiuyong Li

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

1 code implementation11 Mar 2021 Zhaolong Ling, Kui Yu, Yiwen Zhang, Lin Liu, Jiuyong Li

Causal Learner is a toolbox for learning causal structure and Markov blanket (MB) from data.

Dependency-based Anomaly Detection: Framework, Methods and Benchmark

no code implementations13 Nov 2020 Sha Lu, Lin Liu, Jiuyong Li, Thuc Duy Le, Jixue Liu

To show the effectiveness of DepAD, we compare two DepAD methods with nine state-of-the-art anomaly detection methods, and the results show that DepAD methods outperform comparison methods in most cases.

Unsupervised Anomaly Detection

Learning causal representations for robust domain adaptation

no code implementations12 Nov 2020 Shuai Yang, Kui Yu, Fuyuan Cao, Lin Liu, Hao Wang, Jiuyong Li

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.

Domain Adaptation

Assessing Classifier Fairness with Collider Bias

no code implementations8 Oct 2020 Zhenlong Xu, Jixue Liu, Debo Cheng, Jiuyong Li, Lin Liu, Ke Wang, Ziqi Xu, Zhenlong Xu contributed equally to this paper

The increasing application of machine learning techniques in everyday decision-making processes has brought concerns about the fairness of algorithmic decision-making.

Decision Making Fairness

Sufficient Dimension Reduction for Average Causal Effect Estimation

no code implementations14 Sep 2020 Debo Cheng, Jiuyong Li, Lin Liu, Jixue Liu

Having a large number of covariates can have a negative impact on the quality of causal effect estimation since confounding adjustment becomes unreliable when the number of covariates is large relative to the samples available.

Dimensionality Reduction

A unified survey of treatment effect heterogeneity modeling and uplift modeling

no code implementations14 Jul 2020 Weijia Zhang, Jiuyong Li, Lin Liu

A central question in many fields of scientific research is to determine how an outcome would be affected by an action, or to measure the effect of an action (a. k. a treatment effect).


Computational methods for cancer driver discovery: A survey

no code implementations2 Jul 2020 Vu Viet Hoang Pham, Lin Liu, Cameron Bracken, Gregory Goodall, Jiuyong Li, Thuc Duy Le

Due to the complexity of the mechanistic insight of cancer genes in driving cancer and the fast development of the field, it is necessary to have a comprehensive review about the current computational methods for discovering different types of cancer drivers.

Driver Identification

A general framework for causal classification

no code implementations25 Mar 2020 Jiuyong Li, Weijia Zhang, Lin Liu, Kui Yu, Thuc Duy Le, Jixue Liu

We also propose a general framework for causal classification, by using off-the-shelf supervised methods for flexible implementations.

Classification Decision Making +2

Towards unique and unbiased causal effect estimation from data with hidden variables

no code implementations24 Feb 2020 Debo Cheng, Jiuyong Li, Lin Liu, Kui Yu, Thuc Duy Lee, Jixue Liu

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.

Treatment effect estimation with disentangled latent factors

2 code implementations29 Jan 2020 Weijia Zhang, Lin Liu, Jiuyong Li

Much research has been devoted to the problem of estimating treatment effects from observational data; however, most methods assume that the observed variables only contain confounders, i. e., variables that affect both the treatment and the outcome.

Variational Inference

Causal query in observational data with hidden variables

no code implementations28 Jan 2020 Debo Cheng, Jiuyong Li, Lin Liu, Jixue Liu, Kui Yu, Thuc Duy Le

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

1 code implementation17 Nov 2019 Kui Yu, Xianjie Guo, Lin Liu, Jiuyong Li, Hao Wang, Zhaolong Ling, Xindong Wu

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.

Linking Graph Entities with Multiplicity and Provenance

no code implementations13 Aug 2019 Jixue Liu, Selasi Kwashie, Jiuyong Li, Lin Liu, Michael Bewong

The graph model is versatile, thus, it is capable of handling multiple values for an attribute or a relationship, as well as the provenance descriptions of the values.

Data Integration Entity Linking +4

Identify treatment effect patterns for personalised decisions

no code implementations14 Jun 2019 Jiuyong Li, Lin Liu, Shisheng Zhang, Saisai Ma, Thuc Duy Le, Jixue Liu

The existing interpretable modelling methods take a top-down approach to search for subgroups with heterogeneous treatment effects and they may miss the most specific and relevant context for an individual.

Decision Making

Robust Multi-instance Learning with Stable Instances

no code implementations13 Feb 2019 Weijia Zhang, Jiuyong Li, Lin Liu

Multi-instance learning (MIL) deals with tasks where data is represented by a set of bags and each bag is described by a set of instances.

Causal Inference Image Classification

An exploration of algorithmic discrimination in data and classification

no code implementations6 Nov 2018 Jixue Liu, Jiuyong Li, Feiyue Ye, Lin Liu, Thuc Duy Le, Ping Xiong

The paper uses real world data sets to demonstrate the existence of discrimination and the independence between the discrimination of data sets and the discrimination of classification models.

Classification General Classification

FairMod - Making Predictive Models Discrimination Aware

no code implementations5 Nov 2018 Jixue Liu, Jiuyong Li, Lin Liu, Thuc Duy Le, Feiyue Ye, Gefei Li

It models the post-processing of predictions problem as a nonlinear optimization problem to find best adjustments to the predictions so that the discrimination constraints of all protected variables are all met at the same time.

General Classification

Discovering Context Specific Causal Relationships

no code implementations20 Aug 2018 Saisai Ma, Jiuyong Li, Lin Liu, Thuc Duy Le

With the increasing need of personalised decision making, such as personalised medicine and online recommendations, a growing attention has been paid to the discovery of the context and heterogeneity of causal relationships.

Causal Inference Decision Making +1

A Unified View of Causal and Non-causal Feature Selection

no code implementations16 Feb 2018 Kui Yu, Lin Liu, Jiuyong Li

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

no code implementations25 Jan 2018 Kui Yu, Lin Liu, Jiuyong Li

In this paper, we study the problem of discovering the Markov blanket (MB) of a target variable from multiple interventional datasets.

Building Diversified Multiple Trees for Classification in High Dimensional Noisy Biomedical Data

no code implementations18 Dec 2016 Jiuyong Li, Lin Liu, Jixue Liu, Ryan Green

It is common that a trained classification model is applied to the operating data that is deviated from the training data because of noise.

General Classification

A Review on Algorithms for Constraint-based Causal Discovery

no code implementations12 Nov 2016 Kui Yu, Jiuyong Li, Lin Liu

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.

Causal Discovery

ParallelPC: an R package for efficient constraint based causal exploration

no code implementations11 Oct 2015 Thuc Duy Le, Tao Hoang, Jiuyong Li, Lin Liu, Shu Hu

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

Mining Combined Causes in Large Data Sets

no code implementations28 Aug 2015 Saisai Ma, Jiuyong Li, Lin Liu, Thuc Duy Le

A straightforward approach to uncovering a combined cause is to include both individual and combined variables in the causal discovery using existing methods, but this scheme is computationally infeasible due to the huge number of combined variables.

Causal Discovery

From Observational Studies to Causal Rule Mining

no code implementations16 Aug 2015 Jiuyong Li, Thuc Duy Le, Lin Liu, Jixue Liu, Zhou Jin, Bingyu Sun, Saisai Ma

Specifically, association rule mining can be used to deal with the high-dimensionality problem while observational studies can be utilised to eliminate non-causal associations.

Causal Discovery

Causal Decision Trees

no code implementations16 Aug 2015 Jiuyong Li, Saisai Ma, Thuc Duy Le, Lin Liu, Jixue Liu

Classification methods are fast and they could be practical substitutes for finding causal signals in data.

Causal Discovery Causal Inference +2

A fast PC algorithm for high dimensional causal discovery with multi-core PCs

no code implementations9 Feb 2015 Thuc Duy Le, Tao Hoang, Jiuyong Li, Lin Liu, Huawen Liu

However, runtime of the PC algorithm, in the worst-case, is exponential to the number of nodes (variables), and thus it is inefficient when being applied to high dimensional data, e. g. gene expression datasets.

Causal Discovery Causal Inference

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