Texas A&M University 3 credits AGEC
Regression based causal models are limited in their capacity in identifying causal inference patterns among set of variables in complex agribusiness systems where multitude of variables interact. A vast majority of work in data science in agribusiness encompasses the use of observational data in modeling and forecasting decision-making variables. Advances in artificial intelligence and machine learning have helped develop causal inference models based on probabilistic graphical models to uncover the interaction in complex system of variables in agribusiness. Prerequisites: Grade of C or better in AGEC 317 or equivalent; junior or senior classification.
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