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.
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.
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 • 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.
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.
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 • 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 • 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 • 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 • 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 • 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 • 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 • 2 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.
no code implementations • 31 May 2023 • Wenting Ye, Chen Li, Yang Xie, Wen Zhang, Hong-Yu Zhang, Bowen Wang, Debo Cheng, Zaiwen Feng
Identifying and discovering drug-target interactions(DTIs) are vital steps in drug discovery and development.
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.
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 • 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 • 4 Apr 2024 • Chen Li, Huidong Tang, Peng Ju, Debo Cheng, Yasuhiko Morimoto
Aspect-based sentiment analysis aims to predict sentiment polarity with fine granularity.