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Probabilistic Structural Equation Models (PSEM), based on machine-learned Bayesian networks, provide an efficient alternative to traditional Structural Equation Models (SEM). With BayesiaLab 5.0.5, PSEMs can be created through a series of semi-automatic steps, which allow analysts to perform driver analysis extremely quickly, reducing research time from “months to minutes.” This webinar demonstrates a complete workflow for a typical application in Satisfaction Analysis. Dr. Lionel Jouffe and Stefan Conrady present several updates to the approach originally described in their white paper, Driver Analysis & Product Optimization. This includes an illustration of Direct Effects computed by means of Likelihood Matching. They use these direct effects for computing the impact of an action on a given driver, while maintaining all the other drivers probability distributions identical. It is indeed a mean to come back to the framework of Controlled Experiments.
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