Causal inference for time series
Many research questions in Earth and environmental sciences are inherently causal,
requiring robust analyses to establish whether and how changes in one variable cause …
requiring robust analyses to establish whether and how changes in one variable cause …
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 …
Survey and evaluation of causal discovery methods for time series
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 …
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 …
Causal inference for time series analysis: Problems, methods and evaluation
Time series data are a collection of chronological observations which are generated by
several domains such as medical and financial fields. Over the years, different tasks such as …
several domains such as medical and financial fields. Over the years, different tasks such as …
High-recall causal discovery for autocorrelated time series with latent confounders
We present a new method for linear and nonlinear, lagged and contemporaneous constraint-
based causal discovery from observational time series in the presence of latent …
based causal discovery from observational time series in the presence of latent …
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 …
A causal-temporal graphic convolutional network (CT-GCN) approach for TBM load prediction in tunnel excavation
This research proposes a novel deep learning approach named causal-temporal graphic
convolutional network (CT-GCN) which aims to provide accurate predictions on tunnel …
convolutional network (CT-GCN) which aims to provide accurate predictions on tunnel …
Artificial intelligence and healthcare: Forecasting of medical bookings through multi-source time-series fusion
Abstract Nowadays, Artificial intelligence (AI), combined with the digitalization of healthcare,
can lead to substantial improvements in Patient Care, Disease Management, Hospital …
can lead to substantial improvements in Patient Care, Disease Management, Hospital …
Incremental causal graph learning for online root cause analysis
The task of root cause analysis (RCA) is to identify the root causes of system faults/failures
by analyzing system monitoring data. Efficient RCA can greatly accelerate system failure …
by analyzing system monitoring data. Efficient RCA can greatly accelerate system failure …