BayesiaLab
Supervised Elicitation Model for Causal Analysis of Companies Performance

Supervised Elicitation Model for Causal Analysis of Companies' Performance

Presented at the 9th Annual BayesiaLab Conference on October 15, 2021.

Abstract

We propose a decision support system that introduces “supervised elicitation,” an approach in machine learning and AI for elicitation practices. Thanks to a semi-automatic initialization of the causal analysis process, it alleviates the domain experts' workload and shortens the duration of iterative analysis, producing a disruptive innovation.

Supervised elicitation involves BayesiaLab earlier in the process, coupled with complementary methods borrowed from network science. Iteratively applying the method to a dataset of about 700 variables, we retained 100 decisive variables elicited for causal analysis.

The team IQ&AI implemented supervised elicitation for a multinational company willing to obtain an accurate and global insight into its performance factors from high-dimensional and sparse data sets.

Presentation Video

Presentation Slides

About the Presenters

Joel Païn has held managing positions all along his 25+ year career. More specifically, he created and managed two firms (Evaneo and Up & Up) and has been the CEO of several companies and investment firms (Positive Planet, CroiSens, SPA, FinanCités…). He has also acquired extensive experience in strategy consulting and restructuring: he has led many strategy analysis, strategy, and restructuring consulting assignments (with Deloitte, EY, and Up & Up). On these occasions, he has had the opportunity to measure the gap between the way consulting firms deliver strategic consultancy and the kind of answers and level of quality of service clients really expect to receive. He is convinced that bridging this gap is an issue, that can be at least partially solved thanks to new methodologies (IQ & AI), based on AI, experts, and Bayesian networks.

Joel Païn

In 1984, Christophe Thovex started software programming while studying music in Paris, his first professional career until he turned 30. Since 2000, he worked as a consultant, analyst-programmer, engineer in industrial information systems, before getting involved in network science with a Ph.D. thesis (2009-12), 3 years after it was recognized by the US Research Council (2006). He has delivered numerous analyses, software codes, support services, reports, and research outcomes for various SMEs, large companies, French institutions, and territorial authorities – e.g., MAIF, Alstom Marine, Keolis, Bouygues Telecom, Bonduelle, APEC, or Rennes Métropole. As the main author of about 30 scientific publications since 2010, he still collaborates with the French “Conseil National pour la Recherche Scientifique” (CNRS) and to program committees/editorial boards for international conferences and journals (IEEE/ACM).

Christophe Thovex

With more than 20 years of experience as a statistician and information system analyst, Emmanuel Keita is passionate about building bridges between expertise and data analysis, IQ, and AI, and therefore, about BayesiaLab! AI Associate Senior Consultant for Aveyo Consulting (Aveyo.fr), Emmanuel loves popularizing the advantages and fallacies of AI to a large audience (managers and general public) and also giving conferences and lectures to (future) data scientists. Committed to the societal issues of data science, Emmanuel is a National Defense Auditor (France, Prime Minister) and is currently involved in a private blockchain project (Digital seals, Avkee.com).\

Emmanuel Keita


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