One of the fundamental challenges in causal inference is to estimate the...
A predictive model makes outcome predictions based on some given feature...
The instrumental variable (IV) approach is a widely used way to estimate...
In many fields of scientific research and real-world applications, unbia...
This paper studies the problem of estimating the contributions of featur...
Unobserved confounding is the main obstacle to causal effect estimation ...
Anomaly detection is an important research problem because anomalies oft...
Causal effect estimation from observational data is an important but
cha...
Motivation: Uncovering the genomic causes of cancer, known as cancer dri...
This paper discusses the problem of causal query in observational data w...
In personalised decision making, evidence is required to determine suita...
Algorithmic discrimination is an important aspect when data is used for
...
Predictive models such as decision trees and neural networks may produce...
With the increasing need of personalised decision making, such as
person...
Discovering causal relationships from data is the ultimate goal of many
...
In recent years, many methods have been developed for detecting causal
r...
Randomised controlled trials (RCTs) are the most effective approach to c...
Uncovering causal relationships in data is a major objective of data
ana...