Causal/Prescriptive Analytics in Business Decisions


In this talk, I will provide a holistic review of the research methods and tools for causal analytics in business decisions. I focus especially on causal inference in data science. I will discuss a decision tree that helps data scientists to identify the best causal research method based on the problem, context, and the nature of the data. I will draw on my proprietary research on prescriptive analytics ( In addition, to give some high-level overviews of the business use cases, I will also draw insights from use cases in the investment industry, and my previous academic role as a Business Professor leading multiple National Science Foundation-sponsored commercial research projects on AI, including Explainable AI (XAI) and Causal Analytics with Human Expertise. Some of the tools and previews can be found on


Dr. Victor Zitian Chen, CFA, is a believer and action-taker on the idea of a world brain. Dr. Chen is currently the Director of Data Analytics and Insights, Experimental Design and Causal Inference at Fidelity Investments. He leads the causal analytics efforts across the personal investing business at the Fidelity, including experimentation, prescriptive analytics, and causal knowledge graph-based applications. Before joining Fidelity, Dr. Chen was a tenured professor in management and data science at the University of North Carolina, Charlotte, and a visiting professor in international business at Copenhagen Business School, Denmark. He led two major National Science Foundation (NSF) grants focusing on causal knowledge graph-based explainable AI and analytics applications. He founded and led the Global OpenLabs for Performance Enhancement-Analytics and Knowledge System (GoPeaks) – a startup to advance and commercialize knowledge synthesis and causal/prescriptive analytics solutions for business decisions.

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