Indiana University-Purdue University Indianapolis (IUPUI) / The general potential outcomes framework (GPOF) is an essential structure that facilitates clear and coherent specification, identification, and estimation of causal effects. This dissertation utilizes and extends the GPOF, to specify, identify, and estimate causally interpretable (CI) effect parameter (EP) for an outcome of interest that manifests as either a value in a specified subset of the real line or a qualitative event -- a partially qualitative outcome (PQO). The limitations of the conventional GPOF for casting a regression model for a PQO is discussed. The GPOF is only capable of delivering an EP that is subject to a bias due to bad control. The dissertation proposes an outcome measure that maintains all of the essential features of a PQO that is entirely real-valued and is not subject to the bad control critique; the P-weighted outcome – the outcome weighted by the probability that it manifests as a quantitative (real) value. I detail a regression-based estimation method for such EP and, using simulated data, demonstrate its implementation and validate its consistency for the targeted EP. The practicality of the proposed approach is demonstrated by estimating the causal effect of a fully effective policy that bans pregnant women from smoking during pregnancy on a new measure of birth weight. The dissertation also proposes a Generalized Control Function (GCF) approach for modeling and estimating a CI parameter in the context of a fully parametric two-part model (2PM) for a continuous outcome in which the causal variable of interest is continuous and endogenous. The proposed approach is cast within the GPOF. Given a fully parametric specification for the causal variable and under regular Instrumental Variables (IV) assumptions, the approach is shown to satisfy the conditional independence assumption that is often difficult to hold under alternative approaches. Using simulated data, a full information maximum likelihood (FIML) estimator is derived for estimating the “deep” parameters of the model. The Average Incremental Effect (AIE) estimator based on these deep parameter estimates is shown to outperform other conventional estimators. I apply the method for estimating the medical care cost of obesity in youth in the US.

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The aim of this study is to research the possibility of using customer transactional data to identify spending patterns among individuals, that in turn can be used to assess creditworthiness. Two different approaches to unsupervised clustering are used and compared in the study, one being K-means and the other an hierarchical approach. The features used in both clustering techniques are extracted from customer transactional data collected from the customers banks. Internal cluster validity indices and credit scores, calculated by credit institutes, are used to evaluate the results of the clustering techniques. Based on the experiments in this report, we believe that the approach exhibit interesting results and that further research with evaluation on a larger dataset is desired. Proposed future work is to append additional features to the models and study the effect on the resulting clusters. / Målet med detta arbete är att studera möjligheten att använda data om individers kontotransaktioner för att identifiera utgiftsmönster hos individer, som i sin tur kan användas för att utvärdera kreditvärdighet. Två olika tillvägagångssätt som använder oövervakad klustring (eng. unsupervised clustering) används och utvärderas i rapporten, den ena är K-means och den andra är en hierarkisk teknik. De attribut (eng. features) som används i de båda klustrings teknikerna utvinns från data som innehåller kontotransaktioner och som erhålls från banker. Interna kluster värde index (eng. cluster validity indices) och individers riskprognoser, som beräknats av ett kreditinstitut, används för att utvärdera resultaten från klustrings teknikerna. Vi menar att resultaten som presenteras i denna rapport visar att målet till viss del uppnåtts, men att mer data och forskning krävs. Vidare forskning som föreslås är att lägga till fler attribut (eng. features) till modellerna och utvärdera effekten på de resulterande klusterna.

Background: The payment methods available to consumers for online purchases have evolved over time, with options ranging from debit and credit cards to e-wallets like PayPal and Apple Pay. Among these methods, Buy Now Pay Later (BNPL) has emerged as a significant payment method alternative. At the same time the global debt had a record jump between 2021 and 2022, with low-income households being the ones that suffer the most. The convenience offered by BNPL payment method, coupled with the accelerated uptake of this method in recent years, has created regulatory scrutiny concerning its contribution to the financial health of the society.   Purpose: The purpose of this study is to explore the relation between BNPL and financial wellbeing. The Generation Z cohort in the USA was specifically chosen as the target demographic due to their pronounced online purchasing behaviors and the notable tripling of their overall debt within the 2021-2022 period. Methodology: A quantitative approach for collecting data and data analysis was conducted using an online survey. The survey’s questions are influenced by previous research on financial wellbeing and debt levels. The survey’s populations are consisting of 150 individuals from different demographic backgrounds. The survey’s results have been analyzed by writing Python scripts and use relevant statistic libraries. Results and analysis: A significant portion of the study's respondents, irrespective of various demographic factors such as gender, education, and income level, answered that their use of BNPL had little to no impact on their financial wellbeing. Of particular significance is the answers of those familiar with BNPL who did not attribute any deterioration in their financial health by using BNPL. A subset of respondents acknowledged the potential for BNPL to cause overspending or regrettable purchases, but these sentiments were not predominant. Such findings challenge the common belief that BNPL inherently distributes to financial imprudence. However, nuances emerge when examining specific demographics. For instance, male respondents and those with lower educational attainment displayed a slightly heightened propensity to link BNPL with overspending. Conclusions: The overarching narrative suggests that while BNPL might influence purchasing behaviors to some extent, its direct impact on the broader financial wellbeing of individuals is not conclusively negative. Recommendations for future research: Future research can examine deeper behavioral insights on the effects of BNLP, investigate its effects on specific industries (e.g. luxury fashion) or examine global trends (given the present study is focused on USA).

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There is a certain tradition, pageantry, rivalry, and glory in college football. It is well known that college football can be a big time money maker and sometimes covers the costs of other athletic teams within a school. However, it is also recognized that many college football programs lose money or struggle to break even. Thus, there is tremendous variability that exists in the amount of resources a school may have and the outcomes in athletic success, while there is not always a one to one correspondence between the two. The purpose of this study is to examine and analyze the quantifiable determinants of success, considering both financial and nonfinancial variables. The pressure to win, and do so immediately; brand; and outdo other schools in the facility “arm’s race” has reached unprecedented levels.

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