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This course introduces cutting edge causal machine learning methods to provide robust evidence on average and heterogenous treatment effects, while handling confounding in observational data. The course also demonstrates how the resulting estimates of heterogeneous treatment effects can be integrated in a decision modelling framework to support personalized treatment allocation.