Print Email Facebook Twitter Empirical study of GANITE’s robustness to hidden confounders Title Empirical study of GANITE’s robustness to hidden confounders Author van Oudenhoven, Vincent (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Krijthe, J.H. (mentor) Bongers, S.R. (mentor) Bidarra, Rafael (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science and Engineering Project CSE3000 Research Project Date 2022-06-23 Abstract An empirical study is performed exploring the sensitivity to hidden confounders of GANITE, a method for Individualized Treatment Effect (ITE) estimation. Most real world datasets do not measure all confounders and thus it is important to know how crucial this is in order to obtain comparable predictions. This is explored through the removal of confounders with varying strengths and by removing subsets of the confounders simultaneously. The sensitivity is measured through the change in Precision in Estimating Heterogeneous Effects (PEHE) and through the divergence in the estimation of Average Treatment Effect (ATE) from the GT. Experiments are performed on synthetic and semi-synthetic data. The number of removed hidden confounders increases the error and variability of predictions, both for ITE and ATE. The strength of the removed confounders does not show a conclusive relationship on the error metrics. The effect of removing confounders with different causal graphs is explored but fails to show any clear patterns due to the high variance of the results. Subject Machine LearningCausal InferenceSensitivity AnalysisEmpirical ResearchConfoundednessGenerative Adversarial NetworksNeural Networks To reference this document use: http://resolver.tudelft.nl/uuid:3c542b31-b934-470a-b44d-21a370fb9ab0 Part of collection Student theses Document type bachelor thesis Rights © 2022 Vincent van Oudenhoven Files PDF research_paper_GANITE.pdf 2.76 MB Close viewer /islandora/object/uuid:3c542b31-b934-470a-b44d-21a370fb9ab0/datastream/OBJ/view