Causal Inference on Tactical Simulations using Bayesian Structure Learning

Sammanfattning: This thesis explores the possibility of using Bayesian Structure Learning and Do-Calculus to perform causal inference on data from tactical combat simulations provided by Saab. A four-step approach is considered whose first step is to find a Bayesian Network from the data using Bayesian Structure Learning and Probability Distribution Fitting. These Bayesian Networks describe a set of conditional independencies ambiguously. This ambiguity gives rise to a set of feasible Structural Causal Models that describes feasible causal relationships in the data. The approach then continues in its second step by selecting at least one of these Structural Causal Models that can be utilized for performing causal inference using Do-Calculus and Probabilistic Inference in the approach’s third and fourth steps respectively. The thesis concludes that there exist several difficulties with the approach that together with a lack of a methodology for error estimation reduces the method’s reliability. The recommendation is thus to consider the possibility of performing randomized controlled experiments using the tactical simulator before continuing the development of this approach.