Having insight into the causal associations in a complex system facilitates decision making,
eg, for medical treatments, urban infrastructure improvements or financial investments. The …

In this paper, a framework is developed for the identification of causal effects from non-
stationary time series. Focusing on causality measures that make use of delay vectors from …

Root cause diagnosis (RCD) is an important technique for maintaining process safety, which
infers the causalities between faulty measurements to locate the root cause of the fault …

Abstract The 2018 United States Farm Bill has opened the possibility for farmers to increase
their profits through hemp cultivation. The literature suggests hemp has the potential to …

The total electron content (TEC) in the ionosphere widely influences Global Navigation
Satellite Systems (GNSS) especially for critical applications by inducing localized positional …

Due to the high proliferation of distributed energy resources, forecasting ability is an
essential thing for the power system state estimator. In this paper, dynamic state estimation …

Closed-end funds (CEFs) present a unique opportunity to study finance in that the price of
shares rarely matches the net value of the underlying holdings. This study investigates this …

Root cause diagnosis (RCD) is an important technique for maintaining the safe operation of
industrial processes. Traditional RCD methods usually require stationarity assumptions …

We present the Temporal Causal Discovery Framework (TCDF), a deep learning framework
that learns a causal graph structure by discovering causal relationships in observational …

An effective formulation of monetary policy provides an empirical and coherent model of
money related with demand. In order for the monetary authorities to understand the demand …