Big Data in Earth system science and progress towards a digital twin
The concept of a digital twin of Earth envisages the convergence of Big Earth Data with
physics-based models in an interactive computational framework that enables monitoring …
physics-based models in an interactive computational framework that enables monitoring …
Methods and tools for causal discovery and causal inference
Causality is a complex concept, which roots its developments across several fields, such as
statistics, economics, epidemiology, computer science, and philosophy. In recent years, the …
statistics, economics, epidemiology, computer science, and philosophy. In recent years, the …
Discovering causal relations and equations from data
Physics is a field of science that has traditionally used the scientific method to answer
questions about why natural phenomena occur and to make testable models that explain the …
questions about why natural phenomena occur and to make testable models that explain 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 …
Causal discovery in manufacturing: A structured literature review
Industry 4.0 radically alters manufacturing organization and management, fostering
collection and analysis of increasing amounts of data. Advanced data analytics, such as …
collection and analysis of increasing amounts of data. Advanced data analytics, such as …
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 …
Large-scale chemical process causal discovery from big data with transformer-based deep learning
X Bi, D Wu, D **e, H Ye, J Zhao - Process Safety and Environmental …, 2023 - Elsevier
Fault diagnosis is critical for ensuring safe and stable chemical production. Correct
identification of causal relationships among variables in large-scale chemical processes is a …
identification of causal relationships among variables in large-scale chemical processes is a …
Applications of statistical causal inference in software engineering
J Siebert - Information and Software Technology, 2023 - Elsevier
Context: The aim of statistical causal inference (SCI) methods is to estimate causal effects
from observational data (ie, when randomized controlled trials are not possible). In this …
from observational data (ie, when randomized controlled trials are not possible). In this …
Search-and-rescue in the Central Mediterranean Route does not induce migration: Predictive modeling to answer causal queries in migration research
State-and private-led search-and-rescue are hypothesized to foster irregular migration (and
thereby migrant fatalities) by altering the decision calculus associated with the journey. We …
thereby migrant fatalities) by altering the decision calculus associated with the journey. We …