VictorJuiz/thesis huggingface dataset:
Essays in Computational Econometrics

This thesis consists of three chapters, each in the form of a self-contained essay. In contemporary econometric research, computational limitations are often binding. This can be true in theoretical work, where restrictive assumptions are imposed to maintain computational tractability, or in empirical work, where large real-world datasets present computational challenges. Each essay in this thesis presents methods that relate to computational problems arising in econometric theory or applications. In the first essay, an improved Bayesian method for probabilistic record linkage in large matching problems, with an accompanying implementation in R/C++, is presented. The method refines the modelling of comparison data relative to previous methods, allowing the distribution of disagreement among non-matched pairs to be record-specific, leading to dramatic performance improvements in a large application, with accompanying computational improvements. The second essay considers partial identification of counterfactuals in a broad class of models with discrete outcomes and covariates and develops a procedure for computing the identified interval using several new computational tools, including a new algorithm for enumerating the cells induced by hyperplane arrangements. The third essay examines the problem of inference on the value function of a linear program where the right-hand side parameters are random. A tractable method for developing confidence intervals is presented that has asymptotically exact coverage and shows good performance in finite samples.

Keywords: Computation
Econometrics
Advisor: Gu, Jiaying
Eli, Shari
Essays in Household Finance and Education Finance

This dissertation consists of three essays in household finance and education finance. The first essay studies how the portfolio allocation of college savings affects children's college attendance and student debt using novel data on 529 college savings accounts linked with student-level longitudinal data. Employing exogenous variation in the portfolio allocation of 529 accounts driven by institutional details in the asset management sector, the study finds that investing a higher share of college savings in risky assets over a long horizon results in a higher probability of attending a four-year college and lower student debt. The effect is more pronounced for students who receive less financial aid, and its magnitude depends on market performance during the saving period. These results are driven by a positive impact of risk premium on accumulated college savings. The second essay empirically studies how college savings motives affect household stock market participation. More than 65% of households saving for college allocate at least a portion of their college savings to stocks, which are also the most popular class of risky assets for college savings investments. Employing the implementation of a financial aid program that eliminated college saving motives for some military households, the study demonstrates that it reduced the probability of stock market participation outside of retirement accounts for these households by more than a quarter. The study provides additional evidence that college savings motives explain a significant share of stock market participation by demonstrating that only one form of college savings, education savings accounts, accounts for 21% of stock market participation outside of retirement accounts for households with children. The third essay, based on joint work with Claire Celerier and Boris Vallee, examines the determinants of wages in finance academia. By exploiting an extensive dataset covering wages, publications and socio-demographics for 80,000 public-university faculty from all fields, the study shows that tenure-faculty in finance earn wages 50% higher than in other fields. The premium is larger for faculty at highly ranked institutions and for junior faculty. Finance faculty wages are significantly more sensitive to students' future compensation than in other fields. Finance academia is characterized by high university revenues per faculty, a limited supply of relevant profiles, and attractive outside options prior to doctoral studies.

Advisor: Celerier, Claire
Simutin, Mikhail
Developing Fluorescent Chemosensors and Activatable Photosensitizers for Cancer Biology

Aberrant enzyme activity is a hallmark of cancer. Fluorescent chemosensors can directly measure enzyme activity to diagnose cancer, predict resistance to anti-cancer drugs, and to facilitate the discovery of novel anticancer drugs. Moreover, activatable photosensitizers (PS) further exploit aberrant enzyme activity in cancer by activating only in the presence of a cancer over-expressed enzyme. This allows activatable PS to enhance the cancer selectivity of photodynamic therapy (PDT), compared to non-activatable PS. In this work, fluorescent chemosensors and activatable PS were developed for three cancer-relevant enzymes. The first is O6-methylguanine methyltransferase (MGMT), for which fluorescent chemosensors were developed. MGMT activity is a predictor of resistance towards anticancer alkylating agents, making the development of MGMT fluorescent chemosensors highly warranted. We designed, synthesized, and evaluated an array of MGMT fluorescent chemosensors, using synthetic scaffolds based on AA-CW236, a covalent MGMT inhibitor. The chemosensors could measure MGMT activity in-vitro, however, further work needs to be done to improve selectivity for MGMT. Activatable PS for Cytochrome P4501A1 (CYP1A1) were also developed in this work since CYP1A1 is overexpressed in several cancer (including lung, breast, and ovarian). Our approach was to alkylate xanthene PSs at one xanthene hydroxyl, quenching fluorescence and photosensitization. We show that CYP1A1 can reverse this alkylation, thus activating the PSs in the presence of CYP1A1. However, lack of selectivity for CYP1A1 resulted in PDT that had poor selectivity for cells over-expressing CYP1A1. The third enzyme target explored in this work is DNA methyltransferase enzymes (MTases). MTases are responsible for epigenetic regulation, which is highly aberrant in cancer, therefore MTase chemosensors valuable tools to diagnose cancer. We attempted to synthesize aziridine analogues of s-adenosyl-l-methionine (SAM) to transfer a fluorophore to DNA labelled with a good FRET pair. Upon transfer of the SAM analogue to DNA, the resulting FRET signal should correlate with MTase activity. However, the aziridine SAM analogue scaffold proved too reactive, and we could not observe transfer of the SAM analogues to DNA. Therefore, the less reactive “double activated” SAM analogues should be explored in the future.

Keywords: Cytochrome P450
DNA Methyltransferases
Fluorescence Chemosensors
O6-Methylguanine DNA Methyltransferase
Photodynamic Therapy
Advisor: Beharry, Andrew A
In Vivo Effects of Stereotactic Body Radiation Therapy on the Pancreatic Tumor Microenvironment

Despite decades of improvements in cancer therapies, the current standard of care for locally advanced pancreatic cancer (LAPC) provides, on average, only a few months of survival benefit. Stereotactic Body Radiation Therapy (SBRT), a technique that accurately delivers high doses of radiation to tumors, has emerged as a promising therapy to improve local control of LAPC; however, its effects on the tumor microenvironment and hypoxia remain poorly understood. Our group and others have shown that high-dose radiotherapy can cause significant damage to tumor blood vessels. Thus, we hypothesized that SBRT would result in vascular damage that may further lead to tumor hypoxia, potentially generating a more aggressive tumor phenotype. To explore this hypothesis, orthotopic pancreatic tumors in mice were treated with SBRT in 5 daily fractions (8 Gy/fraction, or ‘5×8Gy’) to simulate a typical SBRT dose regimen for LAPC patients. A small animal micro-irradiator with integrated three-dimensional bioluminescence imaging provided accurate spatial targeting for pancreatic tumors in the orthotopic setting. A novel experimental platform using intravital fluorescence microscopy was also developed to enable the longitudinal study of the pancreatic tumor microenvironment in vivo, including real-time quantification of pancreatic tumor cell hypoxia, blood vessels, and collagen structures, at cellular resolution. Contrary to our hypothesis, this platform demonstrated a persistent decrease in pancreatic tumor hypoxia as early as one day after SBRT, with no evidence of vascular damage. This coincided with significant tumor cell death and growth arrest from treatment, indicating that a decrease in the demand for oxygen within the pancreatic tumor microenvironment significantly contributed to its reoxygenation. Overall, the unique combination of a small animal irradiator with the orthotopic intravital imaging platform presented in this thesis provides a novel methodology to investigate the effects of SBRT on the pancreatic tumor microenvironment in vivo. The findings from this work allow us to better understand the mechanisms that influence the tumor response to SBRT and may help to inform clinical practice, potentially guiding the design of future SBRT-based clinical trials.

Keywords: Intravital microscopy
Mouse model
Pancreatic cancer
Stereotactic body radiation therapy
Tumor hypoxia
Tumor microenvironment
Advisor: DaCosta, Ralph S
Descent Techniques in Algebraic K-theory

We investigate two different approaches to describing algebraic K-theory of schemes through descent techniques, one of global nature and the other of local nature. The first half of the thesis is devoted to the study of an adelic descent statement for algebraic K-theory of Noetherian schemes, or more generally for any localizing invariants in place of algebraic K-theory. Given a Noetherian scheme $X$ of finite Krull dimension, Beilinson's cosemisimplicial ring $A^{\bullet}_{\mathrm{red}}(X)$ of reduced adeles on $X$ provides a resolution of the structure sheaf of $X$. We prove that for any localizing invariant $E$ of small stable $\infty$-categories, e.g., nonconnective algebraic K-theory of Bass-Thomason, there is a natural equivalence $E(X)\simeq\lim_{\Delta_{s}}E(A^{\bullet}_{\mathrm{red}}(X))$. This can be viewed as a variant of the formal glueing problem for algebraic K-theory which concerns all irreducible closed subsets at once. We prove the descent statement by first converting the question to a cubical descent statement, and then constructing exact sequences of perfect module categories over adele rings.   In the second half of the thesis, we turn our attention to the study of $p$-adic K-theory of characteristic $p$ rings. Specifically, we provide an alternative proof of Kelly-Morrow's generalization of Geisser-Levine theorem to the Cartier smooth case. Our approach puts emphasis on utilizing motivic filtration and descent spectral sequence. Using the homological smoothness of Cartier smooth rings, we first compute their prismatic cohomology and syntomic cohomology complexes. Through motivic filtration, this computation gives a description of topological cyclic homology for Cartier smooth rings. Then, we use the pro-\'etale descent spectral sequence for topological cyclic homology and rigidity properties of the cyclotomic trace and syntomic cohomology complexes to deduce the result, computing algebraic K-theory of local Cartier smooth rings in terms of their logarithmic de Rham-Witt groups. We also collect some direct consequences of our arguments to prismatic cohomology complexes of Cartier smooth rings and their $p$-torsion free liftings to mixed characteristic.

Advisor: Groechenig, Michael
Role of the Anterior and Mediodorsal Thalamic Nuclei in Cognitive Processes

Our lab encountered a patient with developmental amnesia due to stroke damage to two subcortical regions: the anterior thalamus (AT) and mediodorsal thalamus (MD). To see cognitive deficits after damage to these subcortical regions was somewhat surprising as most research into amnesia historically focused on the hippocampus or prefrontal cortex. The present dissertation thus sought to explore the involvement of these subcortical regions in cognition. In chapter 1 I used functional magnetic resonance imaging to examine activations of the AT and MD of healthy adults during different phases of an associative memory task. I found that the AT showed a greater deactivation during successful memory encoding but that activations of the MD did not show any relation with task accuracy. In chapter 2 I took a different approach in order to address theories that suggest that the MD is important for other forms of cognition such as executive function. I examined recall, recognition, working memory, executive function, and language performance in a group of 22 patients with stroke damage to the thalamus. All cognitive processes were impaired in the thalamic stroke group but there was not a detectable link between which parts of the thalamus were damaged and specific deficits in cognition. Next, I sought to relate these cognitive functions to brain activity patterns in the cortex of these same thalamic stroke patients. Existing theories suggest that the thalamus, particularly the MD, modulates the way cortical regions communicate with each other. In chapter 3 I explored this theory in humans. I examined how patterns of communication between cortical regions as measured by “resting state” functional connectivity were different in thalamic stroke patients and controls. This analysis did not reveal any indication that MD damage disrupts cortico-cortical modulation. It is possible that the MD plays a more prominent role only during specific tasks or at smaller scales. Overall, the results suggest that the AT is involved in associative forms of memory but the role of the MD is less clear. However, a critical first step was made towards understanding the limitations of MD involvement in modulation of cortico-cortical communication.

Keywords: Anterior Thalamus
Cognition
Executive
fMRI
Mediodorsal Thalamus
Memory
Advisor: Olsen, Rosanna K
Design Methods for “Effectively Engaging” Digital Health Behaviour Change Interventions

Digital health interventions are increasingly being designed to support health behaviour changes. Despite the promise of digital health behaviour change interventions to improve health outcomes, there is mixed evidence on whether they are effective in doing so. One key concern is that digital health behaviour change interventions are not “effectively engaging” patient and public users. “Effective engagement” with a digital health behaviour change intervention necessitates that users “micro-engage” with the digital intervention itself (e.g., log-in and use app features) and “macro- engage” with the behaviours the intervention aims to support (e.g., complete physiotherapy exercises). This thesis explored the research question: how can design methods be optimized to support the development of digital health behaviour change interventions that aim to “effectively engage” patients and the public? Paper 1 scoped the literature to examine how digital health design teams can integrate insights from behavioural science and design thinking to develop more “effectively engaging” digital health behaviour change interventions. This paper found that design teams can follow an interdisciplinary, amalgamated approach, referred to as “Behavioural Design Thinking.” Paper 2 used qualitative interviews with digital health design leaders and patient- public partners to explore how to maximize the value of patient-public co-design in the digital health behaviour change design process. This paper found that the value of patient-public co-design extends far beyond product design improvement; patient-public co-design also adds value by ensuring more appropriate implementation planning and more empowering, sustainable partnership building. Paper 3 used qualitative interviews with digital health design leaders to explore how design leaders select and use behavioural science theories, models, and frameworks (TMFs) to guide digital health behaviour change design. This paper found that there is no “magic bullet” behavioural science TMF. The selection of TMFs in digital health behaviour change design is highly nuanced and clearer guidance is needed to help design teams select and use TMFs more meticulously. Overall, this thesis provided a starting point to open the “black box” of digital health behaviour change design methods optimization. Future work is needed to create design methodological guidance that will help ensure digital health interventions are purposefully designed to support “effective engagement.”

Keywords: behavioural science
co-design
design methods
design thinking
digital health
theories and frameworks
Advisor: Kuluski, Kerry
Petch, Jeremy
Multimodal Neuroimaging as a Means to Explore, Refine, and Personalize Neuropsychiatric Deep Brain Stimulation Therapy

Deep brain stimulation (DBS), a neurosurgical intervention in which implanted electrodes are used to modulate dysregulated neural circuits, is a safe and effective therapy for a variety of neurological conditions. Over the last two decades, considerable clinical research has investigated subcallosal cingulate area DBS (SCC-DBS) as a tool for managing severe treatment-resistant depression. This work has produced promising results, with approximately half of treated patients experiencing meaningful clinical improvement (i.e., ‘response’) in open-label trials. Despite this, the ‘failure’ of the largest randomized controlled trial to date and the general difficulty in predicting and optimizing clinical response to SCC-DBS indicate the need for a deeper understanding of this therapy’s mechanism of action and the identification of biomarkers that could guide treatment decisions and improve therapeutic outcomes. This thesis focused on the use of multimodal neuroimaging to characterize the impact of SCC-DBS on brain circuits and to explore factors that explain variance in antidepressant outcome. Our work involved four major components: i) exploring the relationship between stimulation location and clinical outcome using volume of tissue activated modelling and probabilistic stimulation mapping; ii) mapping connectivity patterns associated with efficacious stimulation using normative diffusion-weighted MRI data; iii) investigating potential differences in pre- and post-operative glucose metabolism and brain volume between responders and non-responders; iv) studying acute stimulation-linked changes in brain activity using functional MRI scans acquired under varying DBS conditions. Our results indicated that the precise locus of stimulation within the target region and the engagement of specific white matter tracts are important – albeit incomplete – predictors of clinical outcome. They revealed robust pre-operative differences in brain metabolism and structure between eventual responders and non-responders, suggesting response to SCC-DBS is influenced in part by intrinsic characteristics, and identified how these features changed over months-to-years of chronic stimulation. Finally, they evinced rapid, fMRI-detectable brain activity changes associated with active stimulation that related to long-term outcome and distinguished optimal and suboptimal stimulation settings. Overall, these findings enrich our understanding of the network-level mechanisms of SCC-DBS and proffer candidate biomarkers that might help refine and personalize this therapy through improved patient selection, electrode placement, and stimulation parameter programming.

Keywords: Deep brain stimulation
Depression
Magnetic resonance imaging
Neuroimaging
Psychiatry
Subcallosal cingulate area
Advisor: Lozano, Andres M.
Social, Platform, and Individual Choices in Ride-Sharing

Since the debut of UberPool in August 2014, ride-sharing has become a standard service on various platforms, such as Lyft Shared, GrabShare, and DiDi Hitch. Different from solo ride-sharing services like UberX, shared rides allow platforms to pool two riders in one car. On the one hand, ride-sharing is efficient in increasing platforms' effective capacity with the same fleet of vehicles. On the other hand, ride-sharing may incur negative externality due to compromises on privacy, space, and security. Uber reported over 20% of all rides globally are shared. To generate insights into efficient operations for society, we identify and close the gap between riders' self-interested behavior and the societal preference on shared and solo rides. In Chapter 2, we study three objectives in the ride-sharing economy: 1. volume maximization, which represents the ride-sharing platform’s interest in its growth stage, 2. revenue maximization, which represents the platform’s interest in its sustainment stage, and 3. social welfare maximization, which represents policymakers’ interest. In this chapter, we compare the optimal joining rate and sharing fraction, as well as the corresponding optimal prices of solo and shared rides that induce self-interested riders to reach the optimal goals, among three objectives. In Chapter 3, we analyze and compare the choices by decentralized riders and the centralized social planner of joining rates and sharing probabilities, under First-In-First-Out and Priority-For-Sharing disciplines. We no longer assume that riders automatically reach the most preferable equilibrium automatically. Instead, based on the evolution of equilibrium, we propose social, monetary, and priority schemes to induce riders to reach the preferable initial condition of the system, from which riders converge to the Pareto-dominant equilibrium overtime. In Chapter 4, riders and the platform react to the temporal change in system and make decision based on the real-time queue length (observable queue) and the platform may release or hide this information. We analyze the first-best outcome and riders' equilibrium behaviors and prove that the dynamic pricing is not sufficient to induce self-interested riders to reach the first-best. In addition to the dynamic pricing, we also require dispatching flexibility to fully induce self-interested riders to reach the first-best outcome. In Chapter 5, we perform a numerical experiment using the data from the City of Chicago. We assume that the ride-sharing platforms focuses either on revenue maximization or volume maximization in reality. In both cases, the joining rate and sharing fraction under revenue maximization is lower than the ones under social welfare and volume maximization. In addition, in both cases, social welfare maximization incurs a revenue loss to platforms.

Advisor: Hu, Ming
The Ethics of Intersex Pediatric Surgery

Intersex pediatric surgery is a family of surgical practices performed by pediatric urologists which aim to clarify the sex of an individual with differences/disorders of sexual development, specifically those with ‘intersex’ conditions. It aims to functionally and aesthetically disambiguate an individuals’ binary sex that is obscured by intersex conditions. I ethically reassess intersex pediatric surgery in light of contemporary biotechnology, formal specialization in pediatric urology and long-term data on the efficacy of intersex pediatric surgery, finding that there are only select contexts in which intersex pediatric surgery is morally justifiable. I develop novel, operationalizable and testimonially-informed moral criteria to regulate intersex pediatric surgery, working out the details for how medical professionals in pediatric healthcare facilities are best able to facilitate and augment the decision-making of parents as they decide whether intersex pediatric surgery is in the best interests of their child with intersex conditions.

Keywords: Gender
Health
Healthcare
Identity
Intersex
Sex
Advisor: Mullin, Amy