Papers › Inference for Heterogeneous Treatment Effects with Efficient Instruments and Machine Learning
Inference for Heterogeneous Treatment Effects with Efficient Instruments and Machine Learning
Cyrill Scheidegger, Zijian Guo, Peter Bühlmann
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We introduce a new instrumental variable (IV) estimator for heterogeneous treatment effects in the presence of endogeneity. Our estimator is based on double/debiased machine learning (DML) and uses efficient machine learning instruments (MLIV) and kernel smoothing. We prove consistency and asymptotic normality of our estimator and also construct confidence sets that are more robust towards weak IV. Along the way, we also provide an accessible discussion of the corresponding estimator for the homogeneous treatment effect with efficient machine learning instruments. The methods are evaluated on synthetic and real datasets and an implementation is made available in the R package IVDML.
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