no code implementations • 29 Dec 2024 • Chong Liu, Zaiwen Feng, Lin Liu, Zhenyun Deng, Jiuyong Li, Ruifang Zhai, Debo Cheng, Li Qin
Plausibility Estimation (PE) plays a crucial role for enabling language models to objectively comprehend the real world.
no code implementations • 5 Dec 2024 • Debo Cheng, Jiuyong Li, Lin Liu, Ziqi Xu, Weijia Zhang, Jixue Liu, Thuc Duy Le
Latent confounders are a fundamental challenge for inferring causal effects from observational data.
no code implementations • 26 Nov 2024 • Debo Cheng, Ziqi Xu, Jiuyong Li, Lin Liu, Thuc Duy Le, Xudong Guo, Shichao Zhang
Under the assumptions of the Markov property and availability of proxy variables, we theoretically establish the validity of these learned representations for addressing the biases from time-varying latent confounders, thus enabling accurate causal effect estimation.
no code implementations • 21 Oct 2024 • Debo Cheng, Ziqi Xu, Jiuyong Li, Lin Liu, Kui Yu, Thuc Duy Le, Jixue Liu
In this paper, we will study the conditions when an intuitive model intervention effect has a causal interpretation, i. e., when it indicates whether a feature is a direct cause of the outcome.
no code implementations • 16 Oct 2024 • Zhirong Huang, Shichao Zhang, Debo Cheng, Jiuyong Li, Lin Liu, Guixian Zhang
Specifically, MCDCF treats the multiple items that users interact with and the multiple users that interact with items as treatment variables, enabling it to learn substitutes for the latent confounders that influence the estimation of causality between users and their feedback, as well as between items and user feedback.
no code implementations • 16 Oct 2024 • Jianfeng Deng, Qingfeng Chen, Debo Cheng, Jiuyong Li, Lin Liu, Xiaojing Du
Traditional recommender systems, however, are complicated by confounding bias, particularly in the presence of latent confounders that affect both item exposure and user feedback.
no code implementations • 15 Oct 2024 • Guixian Zhang, Guan Yuan, Debo Cheng, Lin Liu, Jiuyong Li, Shichao Zhang
The message-passing mechanism of GNNs can cause users in the same group to have similar representations due to social homophily, leading model predictions to establish spurious correlations with sensitive attributes.
1 code implementation • 30 Sep 2024 • Wentao Gao, Ziqi Xu, Jiuyong Li, Lin Liu, Jixue Liu, Thuc Duy Le, Debo Cheng, Yanchang Zhao, Yun Chen
As the growing demand for long sequence time-series forecasting in real-world applications, such as electricity consumption planning, the significance of time series forecasting becomes increasingly crucial across various domains.
no code implementations • 30 Sep 2024 • Guixian Zhang, Guan Yuan, Debo Cheng, Lin Liu, Jiuyong Li, Shichao Zhang
The rapid development of Large Language Models (LLMs) creates new opportunities for recommender systems, especially by exploiting the side information (e. g., descriptions and analyses of items) generated by these models.
no code implementations • 19 Sep 2024 • Oscar Blessed Deho, Michael Bewong, Selasi Kwashie, Jiuyong Li, Jixue Liu, Lin Liu, Srecko Joksimovic
Yet still, very little attention has been paid to the problem of naturally occurring changes in data patterns (\textit{aka} data distributional drift), and its impact on fairness algorithms and metrics.
no code implementations • 22 Aug 2024 • Wentao Gao, Jiuyong Li, Debo Cheng, Lin Liu, Jixue Liu, Thuc Duy Le, Xiaojing Du, Xiongren Chen, Yanchang Zhao, Yun Chen
This paper proposes a novel bias correction approach to utilize both GCM and observational data to learn a factor model that captures multi-cause latent confounders.
no code implementations • 21 Aug 2024 • Xiaojing Du, Jiuyong Li, Debo Cheng, Lin Liu, Wentao Gao, Xiongren Chen
Estimating causal effects is crucial for decision-makers in many applications, but it is particularly challenging with observational network data due to peer interactions.
no code implementations • 19 Aug 2024 • Zhirong Huang, Shichao Zhang, Debo Cheng, Jiuyong Li, Lin Liu, Guixian Zhang
In recommender systems, popularity and conformity biases undermine recommender effectiveness by disproportionately favouring popular items, leading to their over-representation in recommendation lists and causing an unbalanced distribution of user-item historical data.
no code implementations • 19 Aug 2024 • Zhirong Huang, Shichao Zhang, Debo Cheng, Jiuyong Li, Lin Liu, Guangquan Lu
CIV4Rec automatically generates valid CIVs and their corresponding conditioning sets directly from interaction data, significantly reducing the complexity of IV selection while effectively mitigating the confounding bias caused by latent variables in recommender systems.
no code implementations • 13 Aug 2024 • Yang Xie, Ziqi Xu, Debo Cheng, Jiuyong Li, Lin Liu, Yinghao Zhang, Zaiwen Feng
In this paper, we propose a novel method of joint Variational AutoEncoder (VAE) and identifiable Variational AutoEncoder (iVAE) for learning the representations of latent confounders and latent post-treatment variables from their proxy variables, termed CPTiVAE, to achieve unbiased causal effect estimation from observational data.
no code implementations • 18 Jun 2024 • Jixue Liu, Jiuyong Li, Stefan Peters, Liang Zhao
To show the advantages of the proposed model, the paper presents extensive results for various possible model architectures improving UNet and draws interesting conclusions including that adding more modules to a model does not always lead to a better performance.
no code implementations • 12 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.
no code implementations • 8 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.
no code implementations • 3 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.
no code implementations • 3 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.
1 code implementation • 21 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.
no code implementations • 24 Apr 2023 • Ziqi Xu, Debo Cheng, Jiuyong Li, Jixue Liu, Lin Liu, Kui Yu
In practice, it is often difficult to identify the set of variables used for front-door adjustment from data.
no code implementations • 10 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.
1 code implementation • 19 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.
no code implementations • 29 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.
no code implementations • 20 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.
no code implementations • 19 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.
no code implementations • 23 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.
1 code implementation • 4 Jun 2022 • Debo Cheng, Jiuyong Li, Lin Liu, Kui Yu, Thuc Duy Lee, Jixue Liu
Based on the theory, we propose a data-driven algorithm to discover a pair of IVs from data.
no code implementations • 11 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.
no code implementations • 23 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.
1 code implementation • 11 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.
no code implementations • 13 Nov 2020 • Sha Lu, Lin Liu, Kui Yu, Thuc Duy Le, Jixue Liu, Jiuyong Li
Anomaly detection is crucial for understanding unusual behaviors in data, as anomalies offer valuable insights.
no code implementations • 12 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.
no code implementations • 8 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.
no code implementations • 14 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.
no code implementations • 14 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).
no code implementations • 2 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.
no code implementations • 25 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.
no code implementations • 24 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.
2 code implementations • 29 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.
no code implementations • 28 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.
1 code implementation • 17 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.
no code implementations • 13 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.
no code implementations • 14 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.
no code implementations • 13 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.
no code implementations • 6 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.
no code implementations • 5 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.
no code implementations • 20 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.
no code implementations • 16 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.
no code implementations • 25 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.
no code implementations • 18 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.
no code implementations • 12 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.
no code implementations • 11 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.
no code implementations • 28 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.
no code implementations • 16 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.
no code implementations • 16 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.
no code implementations • 9 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.