In this work, we describe a new statistical method to improve the detection of treatment effects in interventions. We call our method TAME (Trained Across Multiple Experiments). TAME takes advantage of multiple experiments with similar designs to create a single model. We use this model to predict the outcome of the dependent variable in unseen…

Personalized learning considers that the causal effects of a studied learning intervention may differ for the individual student. Making the inference about causal effects of studies interventions is a central problem. In this paper we propose the Residual Counterfactual Networks (RCN) for answering counterfactual inference questions, such as…