Review of causal discovery methods based on graphical models
A fundamental task in various disciplines of science, including biology, is to find underlying
causal relations and make use of them. Causal relations can be seen if interventions are …
causal relations and make use of them. Causal relations can be seen if interventions are …
A survey of learning causality with data: Problems and methods
This work considers the question of how convenient access to copious data impacts our
ability to learn causal effects and relations. In what ways is learning causality in the era of …
ability to learn causal effects and relations. In what ways is learning causality in the era of …
Survey and evaluation of causal discovery methods for time series
CK Assaad, E Devijver, E Gaussier - Journal of Artificial Intelligence …, 2022 - jair.org
We introduce in this survey the major concepts, models, and algorithms proposed so far to
infer causal relations from observational time series, a task usually referred to as causal …
infer causal relations from observational time series, a task usually referred to as causal …
Artificial intelligence in human resources management: Challenges and a path forward
P Tambe, P Cappelli… - California Management …, 2019 - journals.sagepub.com
There is a substantial gap between the promise and reality of artificial intelligence in human
resource (HR) management. This article identifies four challenges in using data science …
resource (HR) management. This article identifies four challenges in using data science …
Algorithmic recourse: from counterfactual explanations to interventions
As machine learning is increasingly used to inform consequential decision-making (eg, pre-
trial bail and loan approval), it becomes important to explain how the system arrived at its …
trial bail and loan approval), it becomes important to explain how the system arrived at its …
Improving mental health services: A 50-year journey from randomized experiments to artificial intelligence and precision mental health
L Bickman - Administration and Policy in Mental Health and Mental …, 2020 - Springer
This conceptual paper describes the current state of mental health services, identifies critical
problems, and suggests how to solve them. I focus on the potential contributions of artificial …
problems, and suggests how to solve them. I focus on the potential contributions of artificial …
Causal discovery with attention-based convolutional neural networks
Having insight into the causal associations in a complex system facilitates decision making,
eg, for medical treatments, urban infrastructure improvements or financial investments. The …
eg, for medical treatments, urban infrastructure improvements or financial investments. The …
A survey on causal discovery: theory and practice
Understanding the laws that govern a phenomenon is the core of scientific progress. This is
especially true when the goal is to model the interplay between different aspects in a causal …
especially true when the goal is to model the interplay between different aspects in a causal …
Evaluation methods and measures for causal learning algorithms
The convenient access to copious multifaceted data has encouraged machine learning
researchers to reconsider correlation-based learning and embrace the opportunity of …
researchers to reconsider correlation-based learning and embrace the opportunity of …
Causal discovery from temporal data: An overview and new perspectives
Temporal data, representing chronological observations of complex systems, has always
been a typical data structure that can be widely generated by many domains, such as …
been a typical data structure that can be widely generated by many domains, such as …