Application of the Concept of Statistical Causality in Integrable Increasing Processes and Measures

Abstract

In this paper, we investigate an application of the statistical concept of causality, based on Granger’s definition of causality, on raw increasing processes as well as on optional and predictable measures. A raw increasing process is optional (predictable) if the bounded (left-continuous) process X, associated with the measure μA(X), is self-caused. Also, the measure μA(X) is optional (predictable) if an associated process X is self-caused with some additional assumptions. Some of the obtained results, in terms of self-causality, can be directly applied to defining conditions for an optional stopping time to become predictable.

Keywords

• filtration
• statistical causality
• increasing processes
• optional and predictable measures
• predictable stopping time