Essays of instrument-free causal inference, machine learning and marketing applications
department or school: Business, Sauder School of
department or school: Marketing, Division of
data provider: DSpace
degree campus: UBCV
creator: Yang, Fan
issued: 2022-07-31T07:00:00Z
issued: 2022
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: A prominent challenge when drawing causal inference using observational data is the ubiquitous presence of endogenous regressors. This dissertation investigates causal inference and endogeneity correction, both in methodology development and empirical analysis. The first essay (in Chapter 2) develops a new instrument-free method using copula to address the endogeneity problem. The classical econometric method to handle regressor endogeneity requires instrumental variables that must satisfy the stringent condition of exclusion restriction. We use the statistical tool copula to directly model the dependence among the regressors and the error term, and abstract information from existing regressors as a generated regressor added to the outcome regression. Our proposed 2sCOPE method extends the existing copula method to a more general setting by allowing (nearly) normal endogenous regressors and correlated exogenous regressors, and is straightforward to use and broadly applicable. We theoretically prove the consistency and efficiency of 2sCOPE, and demonstrate the performance of 2sCOPE via simulation studies and an empirical application. The second essay (in Chapter 3) further studies the causal inference and endogeneity correction methods for high-dimensional data. The more and more common high-dimensional data in the current big data era make the classical causal inference methods suffer finite-sample bias, inefficiency, or even fail to work when the dimension is larger than the sample size. In this essay, we extend the 2sCOPE method developed in Chapter 2 to the high-dimensional setting, and propose a lasso-based 2sCOPE method. We demonstrate the performance of the proposed method via simulation studies and an empirical application. The third essay (in Chapter 4) empirically studies vertical differentiation in two-sided markets, where network size plays the central role. Vertical differentiation is a common strategy in one-sided markets, but whether it is profitable f…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/82240
note: Essays of Instrument-free Causal Inference, MachineLearning and Marketing ApplicationsbyFan YangB.S., Sun Yat-sen University, 2013Mphil, Hong Kong University of Science and Technology, 2015A THESIS SUBMITTED IN PARTIAL FULFILLMENTOF THE REQUIREMENTS FOR THE DEGREE OFDoctor of PhilosophyinTHE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES(Business Administration - Marketing)The University of British Columbia(Vancouver)July 2022© Fan Yang, 2022The following individuals certify that they have read, and recommend to the Faculty ofGraduate and Postdoctoral Studies for acceptance, the thesis entitled:Essays of Instrument-free Causal Inference, Machine Learning and Mar-keting Applicationssubmitted by Fan Yang in partial fulfillment of the requirements for the degree of Doctorof Philosophy in Business Administration - Marketing.Examining Committee:Professor Yi Qian, Sauder School of Business, The University of British ColumbiaSupervisorProfessor Joey Hoegg, Sauder School of Business, The University of British ColumbiaSupervisory Committee MemberProfessor Ralph Winter, Sauder School of Business, The University of British ColumbiaSupervisory Committee MemberProfessor Hui Xie, Health Sciences, Simon Fraser UniversitySupervisory Committee MemberProfessor Charles Weinberg, Sauder School of Business, The University of British ColumbiaUniversity ExaminerProfessor Kevin Song, Vancouver School of Economics, The University of British ColumbiaUniversity ExamineriiAbstractA prominent challenge when drawing causal inference using observational data is the ubiq-uitous presence of endogenous regressors. This dissertation investigates causal inferenceand endogeneity correction, both in methodology development and empirical analysis.The first essay (in Chapter 2) develops a new instrument-free method using copula toaddress the endogeneity problem. The classical econometric method to handle regressorendogeneity requires instrumental variables that must satisfy the stringent condition of ex-clu…
has type: Thesis/Dissertation
date issued: 2022-11
is shown at: 10.14288/1.0416555
language: eng
degree discipline: Business Administration - Marketing
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Qian, Yi
type: Text
identifier URI: http://hdl.handle.net/2429/82240