Search Results for author: Debo Cheng

Found 12 papers, 2 papers with code

Ripple Knowledge Graph Convolutional Networks For Recommendation Systems

no code implementations2 May 2023 Chen Li, Yang Cao, Ye Zhu, Debo Cheng, Chengyuan Li, Yasuhiko Morimoto

Using knowledge graphs to assist deep learning models in making recommendation decisions has recently been proven to effectively improve the model's interpretability and accuracy.

Knowledge Graphs Recommendation Systems

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

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

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

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.

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