Inference on local causality and tests of non-causality in time series
نویسندگان
چکیده
منابع مشابه
On Causality Inference in Time Series
Causality discovery has been one of the core tasks in scientific research since the beginning of human scientific history. In the age of data tsunami, the task could involve millions of variables, which cannot be achieved feasibly by human. However, the causal discovery using artificial intelligence and statistical techniques in non-experimental settings faces several challenges. In this work, ...
متن کاملNon-causality in bivariate binary time series
In this paper we develop a dynamic discrete-time bivariate probit model, in which the conditions for Granger non-causality can be represented and tested. The conditions for simultaneous independence are also worked out. The model is extended in order to allow for covariates, representing individual as well as time heterogeneity. The proposed model can be estimated by Maximum Likelihood. Granger...
متن کاملCauseMap: fast inference of causality from complex time series
Background. Establishing health-related causal relationships is a central pursuit in biomedical research. Yet, the interdependent non-linearity of biological systems renders causal dynamics laborious and at times impractical to disentangle. This pursuit is further impeded by the dearth of time series that are sufficiently long to observe and understand recurrent patterns of flux. However, as da...
متن کاملGranger Causality Analysis in Irregular Time Series
Learning temporal causal structures between time series is one of the key tools for analyzing time series data. In many real-world applications, we are confronted with Irregular Time Series, whose observations are not sampled at equally-spaced time stamps. The irregularity in sampling intervals violates the basic assumptions behind many models for structure learning. In this paper, we propose a...
متن کاملGranger-causality graphs for multivariate time series
In this paper, we discuss the properties of mixed graphs which visualize causal relationships between the components of multivariate time series. In these Granger-causality graphs, the vertices, representing the components of the time series, are connected by arrows according to the Granger-causality relations between the variables whereas lines correspond to contemporaneous conditional associa...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Electronic Journal of Statistics
سال: 2019
ISSN: 1935-7524
DOI: 10.1214/19-ejs1623