Search Results for author: Jixue Liu

Found 27 papers, 3 papers with code

Instrumental Variable Estimation for Causal Inference in Longitudinal Data with Time-Dependent Latent Confounders

no code implementations12 Dec 2023 Debo Cheng, Ziqi Xu, Jiuyong Li, Lin Liu, Jixue Liu, Wentao Gao, Thuc Duy Le

Causal inference from longitudinal observational data is a challenging problem due to the difficulty in correctly identifying the time-dependent confounders, especially in the presence of latent time-dependent confounders.

Causal Inference

Disentangled Latent Representation Learning for Tackling the Confounding M-Bias Problem in Causal Inference

no code implementations8 Dec 2023 Debo Cheng, Yang Xie, Ziqi Xu, Jiuyong Li, Lin Liu, Jixue Liu, Yinghao Zhang, Zaiwen Feng

To address this problem with co-occurring M-bias and confounding bias, we propose a novel Disentangled Latent Representation learning framework for learning latent representations from proxy variables for unbiased Causal effect Estimation (DLRCE) from observational data.

Causal Inference Representation Learning

Conditional Instrumental Variable Regression with Representation Learning for Causal Inference

no code implementations3 Oct 2023 Debo Cheng, Ziqi Xu, Jiuyong Li, Lin Liu, Jixue Liu, Thuc Duy Le

To address these challenging and practical problems of the standard IV method (linearity assumption and the strict condition), in this paper, we use a conditional IV (CIV) to relax the unconfounded instrument condition of standard IV and propose a non-linear CIV regression with Confounding Balancing Representation Learning, CBRL. CIV, for jointly eliminating the confounding bias from unobserved confounders and balancing the observed confounders, without the linearity assumption.

Causal Inference regression +1

Causal Inference with Conditional Front-Door Adjustment and Identifiable Variational Autoencoder

no code implementations3 Oct 2023 Ziqi Xu, Debo Cheng, Jiuyong Li, Jixue Liu, Lin Liu, Kui Yu

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.

Causal Inference

Learning Conditional Instrumental Variable Representation for Causal Effect Estimation

1 code implementation21 Jun 2023 Debo Cheng, Ziqi Xu, Jiuyong Li, Lin Liu, Thuc Duy Le, Jixue Liu

One of the fundamental challenges in causal inference is to estimate the causal effect of a treatment on its outcome of interest from observational data.

Causal Inference Representation Learning

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 search strategies based on graphical causal modelling to discover useful knowledge from data for causal effect estimation, with some mild assumptions, and has shown promise 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.

counterfactual Fairness +1

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 Complete Knowledge

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

Based on the theory, we develop an algorithm for unbiased causal effect estimation with a given ancestral IV and observational data.

Causal Inference valid

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.

feature selection Unsupervised Anomaly Detection

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.

counterfactual Dimensionality Reduction

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.

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.

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.

Attribute Data Integration +5

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

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

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

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

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

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