144 citations · 407 across the 41 of their papers we have counts for
18 papers · 1 filter
Causal Inference with Conditional Front-Door Adjustment and Identifiable Variational Autoencoder
Ziqi Xu, Debo Cheng, Jiuyong Li +3
An essential and challenging problem in causal inference is causal effect estimation from observational data. The problem becomes more difficult with the presence of unobserved con…
Conditional Instrumental Variable Regression with Representation Learning for Causal Inference
Debo Cheng, Ziqi Xu, Jiuyong Li +3
This paper studies the challenging problem of estimating causal effects from observational data, in the presence of unobserved confounders. The two-stage least square (TSLS) method…
Learning Conditional Instrumental Variable Representation for Causal Effect Estimation
Debo Cheng, Ziqi Xu, Jiuyong Li +3
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. However, causal effect est…
Causal Effect Estimation with Variational AutoEncoder and the Front Door Criterion
Ziqi Xu, Debo Cheng, Jiuyong Li +3
An essential problem in causal inference is estimating causal effects from observational data. The problem becomes more challenging with the presence of unobserved confounders. Whe…
Linking a predictive model to causal effect estimation
Jiuyong Li, Lin Liu, Ziqi Xu +3
A predictive model makes outcome predictions based on some given features, i.e., it estimates the conditional probability of the outcome given a feature vector. In general, a predi…
Disentangled Representation for Causal Mediation Analysis
Ziqi Xu, Debo Cheng, Jiuyong Li +3
Estimating direct and indirect causal effects from observational data is crucial to understanding the causal mechanisms and predicting the behaviour under different interventions.…
Causal Inference with Conditional Instruments using Deep Generative Models
Debo Cheng, Ziqi Xu, Jiuyong Li +3
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…
Disentangled Representation with Causal Constraints for Counterfactual Fairness
Ziqi Xu, Jixue Liu, Debo Cheng +3
Much research has been devoted to the problem of learning fair representations; however, they do not explicitly the relationship between latent representations. In many real-world…
Explanatory causal effects for model agnostic explanations
Jiuyong Li, Ha Xuan Tran, Thuc Duy Le +3
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 fe…
Feature Selection for Efficient Local-to-Global Bayesian Network Structure Learning
Kui Yu, Zhaolong Ling, Lin Liu +2
Local-to-global learning approach plays an essential role in Bayesian network (BN) structure learning. Existing local-to-global learning algorithms first construct the skeleton of…
Causal Learner: A Toolbox for Causal Structure and Markov Blanket Learning
Zhaolong Ling, Kui Yu, Yiwen Zhang +2
Causal Learner is a toolbox for learning causal structure and Markov blanket (MB) from data. It integrates functions for generating simulated Bayesian network data, a set of state-…
Learning causal representations for robust domain adaptation
Shuai Yang, Kui Yu, Fuyuan Cao +3
Domain adaptation solves the learning problem in a target domain by leveraging the knowledge in a relevant source domain. While remarkable advances have been made, almost all exist…
Assessing Classifier Fairness with Collider Bias
Zhenlong Xu, Ziqi Xu, Jixue Liu +5
The increasing application of machine learning techniques in everyday decision-making processes has brought concerns about the fairness of algorithmic decision-making. This paper c…
A general framework for causal classification
Jiuyong Li, Weijia Zhang, Lin Liu +3
In many applications, there is a need to predict the effect of an intervention on different individuals from data. For example, which customers are persuadable by a product promoti…
Treatment effect estimation with disentangled latent factors
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 confoun…
Causality-based Feature Selection: Methods and Evaluations
Kui Yu, Xianjie Guo, Lin Liu +4
Feature selection is a crucial preprocessing step in data analytics and machine learning. Classical feature selection algorithms select features based on the correlations between p…
Robust Multi-instance Learning with Stable Instances
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. Unlike standard supervised learning, only…
Building Diversified Multiple Trees for Classification in High Dimensional Noisy Biomedical Data
Jiuyong Li, Lin Liu, Jixue Liu +1
It is common that a trained classification model is applied to the operating data that is deviated from the training data because of noise. This paper demonstrates that an ensemble…