Learning counterfactual outcomes of MOOC multiple learning behaviors

Abstract The absence of counterfactual outcomes presents a fundamental challenge in causal inference. However, existing work typically does not apply to multiple learning behaviors of Massive Open Online Courses. This paper proposes a counterfactual representation learning model based on multitask learning, applicable to any dimension, and any type of treatment. The model consists of a potential outcome network and a propensity score encoder, which shares feature information from the base layer. The propensity scores calculated by the encoder are then utilized in the potential outcome network to mitigate selection bias. Experiments based on real‐world data sets demonstrate the superior performance of our model compared with baselines.