Sports organisations must continuously assess how better to meet or exceed consumer expectations and perceptions of their experience in order to maintain and increase the number of spectators and loyal fans attending their sporting events. This study aims to enhance our understanding of which characteristics of a service attribute will best define its quality and impact on spectator behaviour by understanding the causal relationship between perceived service quality (PSQ) and satisfaction.

Dans ma thèse, j'utilise des méthodes récentes de microéconométrie appliquée pour évaluer les impacts des événements majeurs (catastrophe de Fukushima Daiichi, Jeux Olympiques), de l'infrastructure (utilisation des terrains urbains, éoliennes) et des institutions dans les systèmes éducatifs) sur le bien-être individuel, la santé et le comportement. Tout au long de mes articles, j'utilise des données longitudinales sur les ménages, en partie fusionnées avec des données spatiales très détaillées, tout en accordant une attention particulière à l'identification des effets causaux. / In my dissertation, I am using recent methods in applied microeconometrics for policy and programme evaluation to evaluate the impacts of major events (the Fukushima Daiichi disaster, the Olympic Games), infrastructure (urban land use, wind turbines), and institutions (instructional time in education systems) on individual well-being, health, and behaviour. Throughout my papers, I am using longitudinal household data, partly merged with highly detailed spatial data, while paying particular attention at identifying causal effects.

Indiana University-Purdue University Indianapolis (IUPUI) / Fatigue is one of the most common and debilitating symptoms reported by cancer patients, yet it is infrequently diagnosed or treated. Relatively little is understood about its etiology in the cancer context. Recently, as researchers have begun to focus attention on cancer-related fatigue (CRF), depression has emerged as its strongest correlate. Few longitudinal studies have been done, however, to determine whether causal influences between the two symptoms exist. The aim of the current study was to determine whether depression has a causal influence on CRF and whether reciprocal effects exist. The study used a single-group cohort design of longitudinal data from a randomized controlled trial (N = 405) of an intervention for pain and depression in a heterogeneous sample of cancer patients. To be eligible, participants met criteria for clinically significant pain or depression. A hypothesis that depression would influence change in fatigue after 3 months was tested using latent variable cross-lagged panel analysis, a structural equation modeling technique. A second hypothesis was that fatigue would also influence change in depression over time but at a lesser magnitude. Depression and fatigue were strongly correlated in the sample (i.e., baseline correlation of latent variables was 0.72). Although the model showed good fit to the data, χ2 (66, N = 329) = 88.16, p = 0.04, SRMR = 0.030, RMSEA = 0.032, and CFI = 1, neither cross-lagged structural path was significant. The findings suggest that depression had no causal influence on changes in fatigue in this sample, and fatigue did not influence change in depression. The clinical implication is that depression treatment may not be helpful as a treatment for CRF and therefore interventions specifically targeting fatigue may be needed. Future research should include additional waves of data and larger sample sizes.

Risk assessment for natural hazards and financial extreme events requires the statistical analysis of extreme events, often beyond observed levels. The characterization and extrapolation of the probability of rare events rely on assumptions about the extremal dependence type and about the specific structure of statistical models. In this thesis, we develop models with flexible tail dependence structures, in order to provide a reliable estimation of tail characteristics and risk measures. From a methodological perspective, this thesis makes the following novel developments. 1) We propose new copula-based models for multivariate and spatial extremes with flexible tail dependence structures, which are parsimonious and able to bridge smoothly asymptotic dependence and asymptotic independence classes, in both the upper and the lower tails; 2) Moreover, aiming at describing more general dependence structures using graphs, we propose a novel extremal dependence measure called the partial tail-correlation coefficient (PTCC) under the framework of regular variation to learn complex extremal network structures; 3) Finally, we develop a semi-parametric neural-network-based regression model to identify spatial causal effects at all quantile levels (including low and high quantiles). Overall, we make novel contributions to creating new flexible extremal dependence models, developing and implementing novel Bayesian computation algorithms, and taking advantage of machine learning and causal inference principles for modeling extremes. Our novel methodologies are illustrated by a range of applications to financial, climatic, and health data. Specifically, we apply our bivariate copula model to the historical closing prices of five leading cryptocurrencies and estimate the extremal dependence evolution over time, and we use the PTCC to learn the extreme risk network of historical global currency exchange data. Moreover, our multivariate spatial factor copula model is applied to study the upper and lower extremal dependence structures of the daily maximum and minimum air temperature from the state of Alabama in the southeastern United States; and we also apply the PTCC in extreme river discharge network learning for the Upper Danube basin. Finally, we apply the causal spatial quantile regression model in quantifying spatial quantile treatment effects of maternal smoking on extreme low birth weight of newborns in North Carolina, United States.

Estimating causal effects in clinical trials is often complicated by treatment noncompliance and missing outcomes. In time-to-event studies, estimation is further complicated by censoring. Censoring is a type of missing outcome, the mechanism of which may be non-ignorable. While new estimates have recently been proposed to account for noncompliance and missing outcomes, few studies have specifically considered time-to-event outcomes, where even the intention-to-treat (ITT) estimator is potentially biased for estimating causal effects of assigned treatment. In this thesis, we develop a series of parametric potential-outcome (PPO) survival models, for the analysis of randomised controlled trials (RCT) with time-to-event outcomes and noncompliance. Both ignorable and non-ignorable censoring mechanisms are considered. We approach model-fitting from a likelihood-based perspective, using the EM algorithm to locate maximum likelihood estimators. We are not aware of any previous work that addresses these complications jointly. In addition, we give new formulations for the average causal effect (ACE) and the complier average causal effect (CACE) to suit survival analysis. To illustrate the likelihood-based method proposed in this thesis, the HIP breast cancer trial data \citep{Baker98, Shapiro88} were re-analysed using specific PPO-survival models, the Weibull and log-normal based PPO-survival models, which assume that the failure time and censored time distributions both follow Weibull or log-normal distributions. Furthermore, an extended PPO-survival model is also derived in this thesis, which permits investigation into the impact of causal effect after accommodating certain pre-treatment covariates. This is an important contribution to the potential outcomes, survival and RCT literature. For comparison, the Frangakis-Rubin (F-R) model \citep{Frangakis99} is also applied to the HIP breast cancer trial data. To date, the F-R model has not yet been applied to any time-to-event data in the literature.