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RISS 인기검색어
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- 저자 : Obeid, Hassan (검색결과 3 건)
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Obeid, Hassan University of California, Berkeley ProQuest Disser 2022 해외박사(DDOD)
소속기관이 구독 중이 아닌 경우 오후 4시부터 익일 오전 9시까지 원문보기가 가능합니다.
The field of transportation and travel behavior research has long been interested in answering causal questions. Take the recent COVID-19 pandemic as an example: the transportation sector in particular was one of the most heavily impacted sectors, and transportation researchers found themselves with a plethora of questions to answer regarding the current and future impacts of the pandemic on the transportation system. Yet, out of roughly 250 pandemic related transportation research papers we reviewed, only about 10 explicitly reference the causal inference literature and are explicit about their causal designs, despite the fact that a significantly larger portion of those papers are trying to answer causal queries. This disconnect is the motivation behind this dissertation.It is important to acknowledge that transportation researchers have indeed used and contributed to several areas of the causal inference literature. Notable contributions include addressing self-selection bias between residential choice and travel behavior, addressing omitted variable bias through integrated choice and latent variable models (ICLVs), and addressing endogeneity between multiple travel outcomes using joint discrete choice models. Despite those contributions, many advances in the causal inference literature have yet to enter the field of travel demand modeling. There are many reasons for this disconnect, some of which stem from long-rooted beliefs and practices within the travel behavior and demand modeling literature on model development, selection, and validation. For starters, there is a tendency for discrete choice modelers in transportation to assume their models allow for causal interpretations because they rely on a behavioral theory of human decision making, like variations of random utility theory. While this criterion typically entails endogeneity checks between the outcome and the regressors, it overlooks important nuances about the data generating process and sources of variation in the exogenous variables. This is further exacerbated by the heavy reliance in the field of transportation demand modeling on goodness-of-fit statistics and statistical tests of significance when finalizing modeling specifications, making them prone to the issue of "bad controls". For instance, adding post-treatment or mediator variables that are exogenous to the outcome but endogenous to the treatment variable of interest, undermines the causal interpretation of the model coefficients, even if adding those variables results in improved predictive model performance and goodness-of-fit statistics. Finally, causal identification strategies rarely appear in the transportation literature. In the causal inference literature, the analyst states the assumptions before drawing any causal conclusions from the model by explicitly specifying the source of variation in the treatment of interest. Such assumptions are referred to as identification strategies: they are assumptions about the data generating process that, only if true, allow the modeler to interpret the model parameters as causal. Those strategies are rarely explicitly stated in transportation demand models, even though those models are often used to evaluate the impact of policy interventions. To address this gap, this dissertation comprises two parts: 1) a conceptual part, and 2) an applied part. The conceptual part consists of Chapter 1, where I elaborate in detail on the disconnect described above and point to specific examples from the transportation literature where such misconceptions about causality are most evident. Next, I provide a review of the key concepts in causal inference, and give an overview of the main causal identification strategies used in the empirical sections of this dissertation. I also give an overview of causal graphical models, an alternative causal inference framework to the more well-known potential outcomes (PO) framework which has been gaining popularity in recent years. I focus the overview on parts where I believe this framework, and Directed Acyclic Graphs (DAGs) more specifically, are most useful to transportation researchers.The applied part consists of Chapters 2, 3 and 4, where I apply some of the causal identification strategies presented in Chapter 1 to answer three different empirical causal research questions in transportation, two of which rely on observational data, while one involves randomized experiments. Each chapter results in domain-specific empirical contributions in its respective area. The common theme across all three chapters is the explicit focus on estimating causal parameters, the clear statement of the causal identification strategies used to estimate those parameters, and the transparency about the source of variation in the treatment. This is in contrast to the common practice in travel behavior modeling where models are specified and estimated without explicitly stating the assumptions under which the estimated parameters can have causal interpretations, and can often lead to erroneous conclusions and misapplications of those models. In Chapter 2, I quantify the causal effect of telecommuting on travel frequency and distance traveled. This question is motivated by the unprecedented rise of telecommuting in the past two years and. (Abstract shortened by ProQuest).
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Essays in Development Economics and Public Finance
Ur Rehman, Obeid ProQuest Dissertations & Theses University of Mich 2021 해외박사(DDOD)
소속기관이 구독 중이 아닌 경우 오후 4시부터 익일 오전 9시까지 원문보기가 가능합니다.
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