Print Email Facebook Twitter An Empirical Study of Adaptive Kernel Density Estimation in Detecting Distributional Overlap Title An Empirical Study of Adaptive Kernel Density Estimation in Detecting Distributional Overlap Author Chen, Chao (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Krijthe, J.H. (mentor) Degree granting institution Delft University of Technology Programme Computer Science and Engineering Project CSE3000 Research Project Date 2023-06-28 Abstract Given data from an observational study or a randomized experiment, the positivity assumption must hold in order to draw causal relations between the treatment and outcome. However, there is shortage of automatic tools that verify compliance with the positivity assumption. We present tools that uses adaptive and standard kernel density estimation (KDE) methods for validating the assumption. Our empirical analysis of the methods offers insight into when a KDE method can and can not reliably be applied to verify positivity in datasets. Subject PositivityOverlapKernel Density Estimation To reference this document use: http://resolver.tudelft.nl/uuid:ba614218-c043-4869-a643-ffe227a116b9 Part of collection Student theses Document type bachelor thesis Rights © 2023 Chao Chen Files PDF RP_Paper_Final.pdf 1.2 MB Close viewer /islandora/object/uuid:ba614218-c043-4869-a643-ffe227a116b9/datastream/OBJ/view