Phillip Heiler, Department of Economics and Business Economics - CREATES

"Causal Inference under Sample Selection and Missing Data: Co-teacher Intervention Effects on Adolescent Mental Health"

Abstract

This paper is concerned with identification, estimation, and specification testing in causal inference problems when data is selective and/or missing. We leverage recent advances in the literature on graphical and counterfactual methods to provide a unifying framework to jointly study these issues. The approach integrates and connects to prominent model, identification, and testing strategies in the literature on missing data, causal machine learning, panel data analysis, and more. We demonstrate its utility in the context of identification and specification testing in sample selection models and field experiments with attrition. We provide a novel analysis of a large scale cluster-randomized control trial in grade 6 from Denmark using a combination of administrative and survey data. Results suggest that co-teachers provide an effective way of improving student’s internalizing behavior.

Joint with: Helena Skyt Nielsen, Louise Beuchert and Simon Calmar Andersen

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Contact person: Jesper Riis-Vestergaard Sørensen