Workshop 2 on Causal Inference Networks for Financial Markets

Description
Granger causality is a popular technique to evaluate cause-effect relations that has been applied to studying time series data from many domains, such as financial markets, manufacturing, supply chains, neuroscience, nursing, and many more. This technique has several significant limitations, of which we will focus on its instability under subsampling. We discuss co-integration as a confirmatory technique to mitigate against spurious causality. This workshop introduces novel sampling techniques and a smoothing filter to address the subsampling limitations. We study many economic time series and apply network analysis to confirmed causal relationships. This leads to a discussion on more robust Granger causality networks and some applications.

This 3-hour workshop, which is a part of the 2026 AI Applications in Industry Conference, is organized in the form of a tutorial. It aims at introducing practitioners (both from academia and industry) to statistical and econometric techniques that have broad applicability which researchers are working on. During the workshop participants will exchange ideas on limitations of current techniques, go into the details of the novel paradigm that addresses some of these limitations, and discuss areas where the novel paradigm may be applied. Seats for this workshop are very limited. Only 15 spots are available.

Instructor: Charles Mutigwe, D.Tech (Western New England University)

Date: Thursday, September 24, 2026
Time: 10:00am-1:00pm
Venue: Third Floor, AV4 Meeting Room, Federal Reserve Plaza, Boston, MA
Price: $50.00