research
∙
06/21/2023
Learning Conditional Instrumental Variable Representation for Causal Effect Estimation
One of the fundamental challenges in causal inference is to estimate the...
share
research
∙
04/24/2023
Causal Effect Estimation with Variational AutoEncoder and the Front Door Criterion
An essential problem in causal inference is estimating causal effects fr...
research
∙
04/10/2023
Linking a predictive model to causal effect estimation
A predictive model makes outcome predictions based on some given feature...
research
∙
02/19/2023
Disentangled Representation for Causal Mediation Analysis
Estimating direct and indirect causal effects from observational data is...
research
∙
11/29/2022
Causal Inference with Conditional Instruments using Deep Generative Models
The instrumental variable (IV) approach is a widely used way to estimate...
research
∙
08/20/2022
Data-Driven Causal Effect Estimation Based on Graphical Causal Modelling: A Survey
In many fields of scientific research and real-world applications, unbia...
research
∙
08/19/2022
Disentangled Representation with Causal Constraints for Counterfactual Fairness
Much research has been devoted to the problem of learning fair represent...
research
∙
06/23/2022
Explanatory causal effects for model agnostic explanations
This paper studies the problem of estimating the contributions of featur...
research
∙
06/04/2022
Discovering Ancestral Instrumental Variables for Causal Inference from Observational Data
Instrumental variable (IV) is a powerful approach to inferring the causa...
research
∙
01/11/2022
Ancestral instrument method for causal inference without a causal graph
Unobserved confounding is the main obstacle to causal effect estimation ...
research
∙
12/20/2021
Feature Selection for Efficient Local-to-Global Bayesian Network Structure Learning
Local-to-global learning approach plays an essential role in Bayesian ne...
research
∙
03/23/2021
Any Part of Bayesian Network Structure Learning
We study an interesting and challenging problem, learning any part of a ...
research
∙
03/11/2021
Causal Learner: A Toolbox for Causal Structure and Markov Blanket Learning
Causal Learner is a toolbox for learning causal structure and Markov bla...
research
∙
11/13/2020
Dependency-based Anomaly Detection: Framework, Methods and Benchmark
Anomaly detection is an important research problem because anomalies oft...
research
∙
11/12/2020
Learning causal representations for robust domain adaptation
Domain adaptation solves the learning problem in a target domain by leve...
research
∙
10/08/2020
Assessing the Fairness of Classifiers with Collider Bias
The increasing maturity of machine learning technologies and their appli...
research
∙
09/14/2020
Sufficient Dimension Reduction for Average Causal Effect Estimation
Having a large number of covariates can have a negative impact on the qu...
research
∙
07/20/2020
Local search for efficient causal effect estimation
Causal effect estimation from observational data is an important but cha...
research
∙
07/14/2020
A unified survey on treatment effect heterogeneity modeling and uplift modeling
A central question in many fields of scientific research is to determine...
research
∙
07/02/2020
Computational methods for cancer driver discovery: A survey
Motivation: Uncovering the genomic causes of cancer, known as cancer dri...
research
∙
03/25/2020
A general framework for causal classification
In many applications, there is a need to predict the effect of an interv...
research
∙
02/24/2020
Towards precise causal effect estimation from data with hidden variables
Causal effect estimation from observational data is a crucial but challe...
research
∙
01/29/2020
Treatment effect estimation with disentangled latent factors
A pressing concern faced by cancer patients is their prognosis under dif...
research
∙
01/28/2020
Causal query in observational data with hidden variables
This paper discusses the problem of causal query in observational data w...
research
∙
11/17/2019
Causality-based Feature Selection: Methods and Evaluations
Feature selection is a crucial preprocessing step in data analytics and ...
research
∙
08/13/2019
Linking Graph Entities with Multiplicity and Provenance
Entity linking is a fundamental database problem with applicationsin dat...
research
∙
06/14/2019
Identify treatment effect patterns for personalised decisions
In personalised decision making, evidence is required to determine suita...
research
∙
02/13/2019
Distributionally Robust Multi-instance Learning with Stable Instances
Multi-instance learning (MIL) deals with tasks where data consist of set...
research
∙
11/06/2018
An exploration of algorithmic discrimination in data and classification
Algorithmic discrimination is an important aspect when data is used for ...
research
∙
11/05/2018
FairMod - Making Predictive Models Discrimination Aware
Predictive models such as decision trees and neural networks may produce...
research
∙
08/20/2018
Discovering Context Specific Causal Relationships
With the increasing need of personalised decision making, such as person...
research
∙
02/16/2018
A Unified View of Causal and Non-causal Feature Selection
In this paper, we unify causal and non-causal feature feature selection ...
research
∙
01/25/2018
Discovering Markov Blanket from Multiple interventional Datasets
In this paper, we study the problem of discovering the Markov blanket (M...
research
∙
11/12/2016
A Review on Algorithms for Constraint-based Causal Discovery
Causal discovery studies the problem of mining causal relationships betw...
research
∙
10/11/2015
ParallelPC: an R package for efficient constraint based causal exploration
Discovering causal relationships from data is the ultimate goal of many ...
research
∙
08/28/2015
Mining Combined Causes in Large Data Sets
In recent years, many methods have been developed for detecting causal r...
research
∙
08/16/2015
From Observational Studies to Causal Rule Mining
Randomised controlled trials (RCTs) are the most effective approach to c...
research
∙
08/16/2015