Full Citation
Title: Essays in Applied Machine Learning and Causal Inference
Citation Type: Dissertation/Thesis
Publication Year: 2022
ISBN:
ISSN:
DOI:
NSFID:
PMCID:
PMID:
Abstract: This dissertation represents a study of how machine learning can be incorporated into existing econometric causal techniques, with explorations both in the costs and benefits of making that choice. The first chapter explores a simulated instrumental variables setting to evaluate the ease of incorporating unmodified machine learning techniques into the ”first stage“ problem. The first stage of two-stage least squares (2SLS) is a prediction problem—suggesting gains from utilizing ML in 2SLS’s first stage. However, little guidance exists on when ML helps 2SLS—or when it hurts. We investigate the implications of inserting ML into 2SLS, decomposing the bias into three informative components. Mechanically, ML-in-2SLS procedures face issues common to prediction and causal-inference settings—and their interaction. Through simulation, we show linear ML methods (e.g.post-Lasso) work “well,” while nonlinear methods (e.g.random forests, neural nets) generate substantial bias in second-stage estimates—some exceeding the bias of endogenous OLS. This work was performed in conjunction with professors Edward Rubin and Glen Waddell. The chapter author wrote simulation code, excepting the substantial portions used for table creation and to iterate over differing methods, to evaluate and run the methods tested in this chapter, and we iv designed the DGP function based on those found in Belloni, Chen, Chernozhukov, and Hansen (2012).
User Submitted?: No
Authors: Lennon, Connor
Institution: University of Oregon
Department: Economics
Advisor:
Degree:
Publisher Location:
Pages: 1-153
Data Collections: IPUMS NHGIS
Topics: Population Data Science
Countries: