Generic Machine Learning Inference on Heterogeneous Treatment Effects in Randomized Experiments, with an Application to Investment Readiness Programs for Start-Ups and SMEs in the Western Balkans

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Keywords	Machine Learning (ML); Randomized Controlled Trials (RCTs); Heterogeneous Treatment Effects; Investment Readiness Programs (IRPs); Start-Ups; Small and Medium-Sized Enterprises (SMEs); Western Balkans; Average Treatment Effect (ATE); Conditional Average Treatment Effects (CATE); Non-Parametric Inference; Best Linear Predictor (BLP); Sorted Group Average Treatment Effects (GATES); Classification Analysis (CLAN); Causal Inference; High-Dimensional Data; Sensitivity Analysis; Consistent Estimators; Linear Regression; Treatment Effect Heterogeneity
Thesis Advisor	Welz, MFO
Persistent URL	hdl.handle.net/2105/73468
Series	Econometrie
Organisation	Erasmus School of Economics
Citation APA Style AAA Style APA Style Cell Style Chicago Style Harvard Style IEEE Style MLA Style Nature Style Vancouver Style American-Institute-of-Physics Style Council-of-Science-Editors Style BibTex Format Endnote Format RIS Format CSL Format DOIs only Format	Vos, L.P. (2024, September 18). Generic Machine Learning Inference on Heterogeneous Treatment Effects in Randomized Experiments, with an Application to Investment Readiness Programs for Start-Ups and SMEs in the Western Balkans. Econometrie. Retrieved from http://hdl.handle.net/2105/73468

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