144 citations · 407 across the 41 of their papers we have counts for
14 papers · 1 filter
Data-Driven Causal Effect Estimation Based on Graphical Causal Modelling: A Survey
Debo Cheng, Jiuyong Li, Lin Liu +2
In many fields of scientific research and real-world applications, unbiased estimation of causal effects from non-experimental data is crucial for understanding the mechanism under…
Discovering Ancestral Instrumental Variables for Causal Inference from Observational Data
Debo Cheng, Jiuyong Li, Lin Liu +3
Instrumental variable (IV) is a powerful approach to inferring the causal effect of a treatment on an outcome of interest from observational data even when there exist latent confo…
Ancestral Instrument Method for Causal Inference without Complete Knowledge
Debo Cheng, Jiuyong Li, Lin Liu +3
Unobserved confounding is the main obstacle to causal effect estimation from observational data. Instrumental variables (IVs) are widely used for causal effect estimation when ther…
Causal query in observational data with hidden variables
Debo Cheng, Jiuyong Li, Lin Liu +3
This paper discusses the problem of causal query in observational data with hidden variables, with the aim of seeking the change of an outcome when "manipulating" a variable while…
FairMod - Making Predictive Models Discrimination Aware
Jixue Liu, Jiuyong Li, Lin Liu +3
Predictive models such as decision trees and neural networks may produce discrimination in their predictions. This paper proposes a method to post-process the predictions of a pred…
Discovering Context Specific Causal Relationships
Saisai Ma, Jiuyong Li, Lin Liu +1
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 contex…
A Unified View of Causal and Non-causal Feature Selection
Kui Yu, Lin Liu, Jiuyong Li
In this paper, we aim to develop a unified view of causal and non-causal feature selection methods. The unified view will fill in the gap in the research of the relation between th…
Discovering Markov Blanket from Multiple interventional Datasets
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. Datasets attained from interventional experim…
A Review on Algorithms for Constraint-based Causal Discovery
Kui Yu, Jiuyong Li, Lin Liu
Causal discovery studies the problem of mining causal relationships between variables from data, which is of primary interest in science. During the past decades, significant amoun…
ParallelPC: an R package for efficient constraint based causal exploration
Thuc Duy Le, Tao Hoang, Jiuyong Li +2
Discovering causal relationships from data is the ultimate goal of many research areas. Constraint based causal exploration algorithms, such as PC, FCI, RFCI, PC-simple, IDA and Jo…
From Observational Studies to Causal Rule Mining
Jiuyong Li, Thuc Duy Le, Lin Liu +4
Randomised controlled trials (RCTs) are the most effective approach to causal discovery, but in many circumstances it is impossible to conduct RCTs. Therefore observational studies…
Causal Decision Trees
Jiuyong Li, Saisai Ma, Thuc Duy Le +2
Uncovering causal relationships in data is a major objective of data analytics. Causal relationships are normally discovered with designed experiments, e.g. randomised controlled t…
Mining Combined Causes in Large Data Sets
Saisai Ma, Jiuyong Li, Lin Liu +1
In recent years, many methods have been developed for detecting causal relationships in observational data. Some of them have the potential to tackle large data sets. However, thes…
A fast PC algorithm for high dimensional causal discovery with multi-core PCs
Thuc Duy Le, Tao Hoang, Jiuyong Li +2
Discovering causal relationships from observational data is a crucial problem and it has applications in many research areas. The PC algorithm is the state-of-the-art constraint ba…