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 …
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 …
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 …
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 …
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 …
High-recall causal discovery for autocorrelated time series with latent confounders
A Gerhardus, J Runge - Advances in Neural Information …, 2020 - proceedings.neurips.cc
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 …
[PDF][PDF] Advanced data analysis from an elementary point of view
C Shalizi - 2013 - Citeseer
These are the notes for 36-402, Advanced Data Analysis, at Carnegie Mellon. If you are not
enrolled in the class, you should know that it's the methodological capstone of the core …
enrolled in the class, you should know that it's the methodological capstone of the core …
[HTML][HTML] Causal discovery from nonstationary/heterogeneous data: Skeleton estimation and orientation determination
It is commonplace to encounter nonstationary or heterogeneous data, of which the
underlying generating process changes over time or across data sets (the data sets may …
underlying generating process changes over time or across data sets (the data sets may …
Causal recurrent variational autoencoder for medical time series generation
H Li, S Yu, J Principe - Proceedings of the AAAI conference on artificial …, 2023 - ojs.aaai.org
We propose causal recurrent variational autoencoder (CR-VAE), a novel generative model
that is able to learn a Granger causal graph from a multivariate time series x and …
that is able to learn a Granger causal graph from a multivariate time series x and …
Causal inference on time series using restricted structural equation models
Causal inference uses observational data to infer the causal structure of the data generating
system. We study a class of restricted Structural Equation Models for time series that we call …
system. We study a class of restricted Structural Equation Models for time series that we call …