arXiv research

Study reveals dynamic causal relationships between Ethereum transaction fees and economic subsystems.

New framework for dynamic causal graph modeling and effect estimation.

New model predicts energy prices under different scenarios.

MSCT predicts post-crash traffic speed using causal inference.

New method infers causal relationships from nonstationary time series data.

New method learns time-varying home field advantage in football.

Novel method models dynamic brain graphs from time series data.

TAGM models time-varying connections between variables.

New approach for causal inference with interdependent, time-varying latent confounders.

TV-SurvCaus improves causal inference for dynamic treatments in survival analysis.

Temporal Causal Prior-Data Fitted Networks (TCPFN) for industrial time series causal discovery

Framework for Granger causality in extreme events.

Proposes DCNAR for dynamic causal inference from neural time series.

Proposes a method for interpreting time-varying causal effect moderation in high-dimensional data.

Separation of the sources and analysis of their connectivity have been an important topic in EEG/MEG analysis. To solve this problem in an automatic manner, we propose a two-layer model, in which the sources are conditionally uncorrelated from each other, but not independent; the dependence is caused by the causality i…

TCFimt forecasts causal effects of multiple interventions from individual data.

New method estimates causal effects with multi-valued, time-varying treatments.

Estimates causal effects from patient trajectories using DeepACE model.

New method tests independence with single nonstationary time series.

Develops methods for causal inference in longitudinal data.

Develops methods to learn optimal treatment regimes using causal tree methods.

Meta-learning method for estimating time-varying mHealth intervention effects.

New method uses entropy to generate multiple plausible causal maps.

Causality graphs are routinely estimated in social sciences, natural sciences, and engineering due to their capacity to efficiently represent the spatiotemporal structure of multivariate data sets in a format amenable for human interpretation, forecasting, and anomaly detection. A popular approach to mathematically for…

CAST models time-varying treatment effects in cancer patients.

Applying machine learning in the health care domain has shown promising results in recent years. Interpretable outputs from learning algorithms are desirable for decision making by health care personnel. In this work, we explore the possibility of utilizing causal relationships to refine diagnostic prediction. We focus…

A new method clusters heterogeneous subgroups for accurate causal learning.

New algorithm reduces regret in CBs with time-varying models.

New assumptions help identify causal relationships in data.

Develops a Causal Transformer for estimating counterfactual outcomes from longitudinal data.

Proposes a method to identify causal relationships using background knowledge.

Developed a flexible Bayesian g-formula for causal survival analysis with time-dependent confounding.

NOTEARS fails to identify true causal relationships from data.

A time-varying cointegration model for foreign exchange rates is presented. Unlike previous studies, we allow the loading matrix in the vector error correction (VEC) model to be varying over time. Because the loading matrix in the VEC model is associated with the speed at which deviations from the long-run relationship…

We report on time-varying network connectedness within three banking systems: North America, the EU, and ASEAN. The original method by Diebold and Yilmaz is improved by using exponentially weighted daily returns and ridge regularization on vector autoregression (VAR) and forecast error variance decomposition (FEVD). We…

This paper presents a new open source Python framework for causal discovery from observational data and domain background knowledge, aimed at causal graph and causal mechanism modeling. The 'cdt' package implements the end-to-end approach, recovering the direct dependencies (the skeleton of the causal graph) and the ca…

Enhances GNNs for causal relationship learning.

Rhino learns causal relationships from time series data with history-dependent noise.

This review explores causal decision-making to improve decision quality.

We consider the problem of inferring causal relationships between two or more passively observed variables. While the problem of such causal discovery has been extensively studied especially in the bivariate setting, the majority of current methods assume a linear causal relationship, and the few methods which consider…

Deep learning aids causal inference in complex settings.

A technique uncovers latent causal relationships in multiple time series data.

Frengression models causal data flexibly and faithfully.

Study finds a method to discover causal relationships that are invariant to marginal distributions.

The discovery of causal relationships is a fundamental problem in science and medicine. In recent years, many elegant approaches to discovering causal relationships between two variables from observational data have been proposed. However, most of these deal only with purely directed causal relationships and cannot det…

A neural network finds causal relationships among latent variables.

New algorithm groups variables by ancestral relationships to improve causal graph estimation accuracy.

Gene regulatory networks play a crucial role in controlling an organism's biological processes, which is why there is significant interest in developing computational methods that are able to extract their structure from high-throughput genetic data. Many of these computational methods are designed to infer individual …