This dissertation investigates novel statistical approaches for causal effect estimation in observational settings, where controlled experimentation is infeasible and confounding is the main hurdle in estimating causal effect. As such, deconfounding constructs the main subject of this dissertation, that is (i) to restore the covariate balance between treatment groups and (ii) to attenuate spurious correlations in training data to derive valid causal conclusions that generalize. By incorporating ideas from representation learning, adversarial matching, generative causal estimation, and invariant risk modeling, this dissertation establishes a causal framework that balances the covariate distribution in latent representation space to yield individualized estimations, and further contributes novel perspectives on causal effect estimation based on invariance principles. The dissertation begins with a systematic review and examination of classical propensity score based balancing schemes for population-level causal effect estimation, presented in Chapter 2. Three causal estimands that target different foci in the population are considered: average treatment effect on the whole population (ATE), average treatment effect on the treated population (ATT), and average treatment effect on the overlap population (ATO). The procedure is demonstrated in a naturalistic driving study (NDS) to evaluate the causal effect of cellphone distraction on crash risk. While highlighting the importance of adopting causal perspectives in analyzing risk factors, discussions on the limitations in balance efficiency, robustness against high-dimensional data and complex interactions, and the need for individualization are provided to motivate subsequent developments. Chapter 3 presents a novel generative Bayesian causal estimation framework named Balancing Variational Neural Inference of Causal Effects (BV-NICE). Via appealing to the Robinson factorization and a latent Bayesian model, a novel variational bound on likelihood is derived, explicitly characterized by the causal effect and propensity score. Notably, by treating observed variables as noisy proxies of unmeasurable latent confounders, the variational posterior approximation is re-purposed as a stochastic feature encoder that fully acknowledges representation uncertainties. To resolve the imbalance in representations, BV-NICE enforces KL-regularization on the respective representation marginals using Fenchel mini-max learning, justified by a new generalization bound on the counterfactual prediction accuracy. The robustness and effectiveness of this framework are demonstrated through an extensive set of tests against competing solutions on semi-synthetic and real-world datasets. In recognition of the reliability issue when extending causal conclusions beyond training distributions, Chapter 4 argues ascertaining causal stability is the key and introduces a novel procedure called Risk Invariant Causal Estimation (RICE). By carefully re-examining the relationship between statistical invariance and causality, RICE cleverly leverages the observed data disparities to enable the identification of stable causal effects. Concretely, the causal inference objective is reformulated under the framework of invariant risk modeling (IRM), where a population-optimality penalty is enforced to filter out un-generalizable effects across heterogeneous populations. Importantly, RICE allows settings where counterfactual reasoning with unobserved confounding or biased sampling designs become feasible. The effectiveness of this new proposal is verified with respect to a variety of study designs on real and synthetic data. In summary, this dissertation presents a flexible causal inference framework that acknowledges the representation uncertainties and data heterogeneities. It enjoys three merits: improved balance to complex covariate interactions, enhanced robustness to unobservable latent confounders, and better generalizability to novel populations. / Doctor of Philosophy / Reasoning cause and effect is the innate ability of a human. While the drive to understand cause and effect is instinct, the rigorous reasoning process is usually trained through the observation of countless trials and failures. In this dissertation, we embark on a journey to explore various principles and novel statistical approaches for causal inference in observational studies. Throughout the dissertation, we focus on the causal effect estimation which answers questions like ``what if" and ``what could have happened". The causal effect of a treatment is measured by comparing the outcomes corresponding to different treatment levels of the same unit, e.g. ``what if the unit is treated instead of not treated?". The challenge lies in the fact that i) a unit only receives one treatment at a time and therefore it is impossible to directly compare outcomes of different treatment levels; ii) comparing the outcomes across different units may involve bias due to confounding as the treatment assignment potentially follows a systematic mechanism. Therefore, deconfounding constructs the main hurdle in estimating causal effects. This dissertation presents two parallel principles of deconfounding: i) balancing, i.e., comparing difference under similar conditions; ii) contrasting, i.e., extracting invariance under heterogeneous conditions. Chapter 2 and Chapter 3 explore causal effect through balancing, with the former systematically reviews a classical propensity score weighting approach in a conventional data setting and the latter presents a novel generative Bayesian framework named Balancing Variational Neural Inference of Causal Effects(BV-NICE) for high-dimensional, complex, and noisy observational data. It incorporates the advance deep learning techniques of representation learning, adversarial learning, and variational inference. The robustness and effectiveness of the proposed framework are demonstrated through an extensive set of experiments. Chapter 4 extracts causal effect through contrasting, emphasizing that ascertaining stability is the key of causality. A novel causal effect estimating procedure called Risk Invariant Causal Estimation(RICE) is proposed that leverages the observed data disparities to enable the identification of stable causal effects. The improved generalizability of RICE is demonstrated through synthetic data with different structures, compared with state-of-art models. In summary, this dissertation presents a flexible causal inference framework that acknowledges the data uncertainties and heterogeneities. By promoting two different aspects of causal principles and integrating advance deep learning techniques, the proposed framework shows improved balance for complex covariate interactions, enhanced robustness for unobservable latent confounders, and better generalizability for novel populations.

Interpersonal trust reflects a vital component of all social relationships. Trust has been linked to a wide variety of individual and group outcomes in the literature, including personal satisfaction and motivation, willingness to take risks, and organizational success (Dirks & Ferrin, 2001; Pratt & Dirks, 2007; Simpson, 2007). In this dissertation I tested a new conceptual model evaluating the roles of attachment, propensity to trust, perceived similarity of trustee to self, and social exchange processes in trust development with randomly assigned, same-sex undergraduate roommates. Two hundred and fourteen first-year students (60% female, 85% Caucasian, mean age = 18) at a large south-eastern university completed self-report measures once per week during the first five weeks of the fall semester. Perceived similarity measured the second week of classes and social exchange measured three weeks later combined to provide the best prediction of participants' final trust scores. Attachment and propensity to trust, more distal predictors, did not have a significant relationship with trust. This study demonstrated that trust is strongly related to perceived similarity, as well as social exchange. A prime contribution of this study is the longitudinal, empirical test of a model of trust development in a new and meaningful relationship. Future work may build on this research design and these findings by focusing on early measurement of constructs, measuring dyads rather than individuals, and incorporating behavioral measures of trust. / Ph. D.

Blue bonds are gaining global traction as innovative financial instruments to tackle marine sustainability, yet their yield spreads compared to conventional bonds remain unexplored. Based on the growing interest in sustainable investments and the concept of the greenium, this study introduces and searches for a bluenium, the analogous premium for blue bonds. Hence, the purpose of this research is to investigate whether blue bonds exhibit a lower yield at issuance compared to conventional bonds. This examination is intended to contribute to the literature on impact investment risk and return, particularly in the context of marine sustainability, providing valuable insights for investors, issuers, researchers, and policymakers. The study employs the propensity score matching (PSM) method to ensure robust comparative analysis between blue bonds and comparable conventional bonds. The empirical analysis identifies a yield spread of 47 basis points (bps) favouring higher yields for blue bonds, though these results lack statistical significance. Hence, there is no significant evidence of lower yields for blue bonds compared to conventional bonds. The insignificant results could stem from the relatively small sample size, reflecting the fact that blue bonds are in their early stage, suggesting that they may require further development, similar to what green bonds experienced. Future research should consider larger samples and additional variables to enhance the robustness and applicability of the findings. This study informs stakeholders of the complexities and development potential of the blue bond market.

Contract selection is at the forefront of risk management and mitigation, yet it is an underrepresented area of research in supply chain management field as well as the influences of individual-level risk propensity and risk perception on supply chain decision-making processes. This dissertation explores effects of managerial risk propensity and risk perception on contract selection through the theoretical lens of Transaction Cost Economics (TCE), using a vignette-based experimental research design. This body of work introduces both a first-ever systemmigram of TCE in relation to contract selection, and a novel measurement scale for TCE contract typology. Furthermore, this dissertation tests the TCE predictions towards contract selection and explores the moderating role of financial risk propensity and risk perception (cost vs. supplier performance) on contract selection. The main theoretical contribution of this research is the opening of an old debate on the risk neutrality assumption of TCE, by providing empirical evidence that individual-level risk propensity and perception effect contract selection. The practical implications are significant and points out to the need for a better fit between individual-level and firm-level risk propensity.

Darbo tikslas – ištirti ekspatriantų pasitenkinimo darbu įtaką ketinimui išeiti iš įmonės. Darbo tikslui pasiekti buvo iškelti uždaviniai išanalizuoti ekspatriantų valdymo esmę, nustatyti ekspatriantų pasitenkinimą darbu įtakojančius veiksnius, išnagrinėti ketinimą palikti organizaciją įtakojančius veiksnius, empiriškai patikrinti ekspatriantų pasitenkinimo darbu įtaką ketinimui palikti įmonę. Darbo pradžioje analizuojama su ekspatriantais, pasitenkinimu darbu ir ketinimu palikti įmonę susijusi literatūra, parenkami literatūroje minimi veiksniai, turintys įtakos ekspatrianto pasitenkinimui darbu. Taip pat literatūros pagrindu konstruojamas teorinis modelis. Vėliau atliktas empirinis tyrimas, kurio tikslas - empiriškai patikrinti siūlomą ekspatriantų kaitos valdymo modelį. Atlikus kokybinį tyrimą buvo suformuluotas galutinis ekspatriantų pasitenkinimą darbu įtakojančių veiksnių sąrašas. Kiekybinio tyrimo metu atlikta ištisinė vienos įmonių grupės darbuotojų apklausa, išanalizuoti modelio veiksnių reikšmių lygiai bei tarpusavio ryšiai. Tyrimas dalinai patvirtino teorinį modelį bei atskleidė ekspatrianto pasitenkinimo lygio ryšį su ketinimu palikti įmonę. Darbo pabaigoje pateikiamos išvados bei praktinė interpretacija vadovams. / The purpose of this work is to explore the influence of expatriate job satisfaction on propensity to leave the company. List of tasks was established in order to achieve the purpose of the master work, i.e. analysis of the essence of expatriates’ management, identification of the factors that influence expatriate job satisfaction, identification of the factors that influence propensity to leave the company, empirical test of the influence of expatriate job satisfaction on propensity to leave the company. Literature about expatriates, job satisfaction and propensity to leave the company was analysed in the first part of the work. Afterwards, elements that have influence on expatriate job satisfaction were selected. Finally, theoretical model based on the literature analysis results was created. Empirical test was executed in order to test proposed expatriate turnover management model. Final list of elements that have influence on expatriate job satisfaction was created as a result of qualitive research. Later on quantitive research was executed during which means of model elements and relations between them were evaluated. Research partly approved the theoretical model and showed that there was relation between expatriate job satisfaction and propensity to leave the company. Conclusions and practical interpretation for managers was proposed at the end.

In Chapter 1 of the dissertation, the role of social networks is analyzed as an important determinant in the search behavior of the unemployed. Based on the hypothesis that the unemployed generate information on vacancies through their social network, search theory predicts that individuals with large social networks should experience an increased productivity of informal search, and reduce their search in formal channels. Due to the higher productivity of search, unemployed with a larger network are also expected to have a higher reservation wage than unemployed with a small network. The model-theoretic predictions are tested and confirmed empirically. It is found that the search behavior of unemployed is significantly affected by the presence of social contacts, with larger networks implying a stronger substitution away from formal search channels towards informal channels. The substitution is particularly pronounced for passive formal search methods, i.e., search methods that generate rather non-specific types of job offer information at low relative cost. We also find small but significant positive effects of an increase of the network size on the reservation wage. These results have important implications on the analysis of the job search monitoring or counseling measures that are usually targeted at formal search only. Chapter 2 of the dissertation addresses the labor market effects of vacancy information during the early stages of unemployment. The outcomes considered are the speed of exit from unemployment, the effects on the quality of employment and the short-and medium-term effects on active labor market program (ALMP) participation. It is found that vacancy information significantly increases the speed of entry into employment; at the same time the probability to participate in ALMP is significantly reduced. Whereas the long-term reduction in the ALMP arises in consequence of the earlier exit from unemployment, we also observe a short-run decrease for some labor market groups which suggest that caseworker use high and low intensity activation measures interchangeably which is clearly questionable from an efficiency point of view. For unemployed who find a job through vacancy information we observe a small negative effect on the weekly number of hours worked. In Chapter 3, the long-term effects of participation in ALMP are assessed for unemployed youth under 25 years of age. Complementary to the analysis in Chapter 2, the effects of participation in time- and cost-intensive measures of active labor market policies are examined. In particular we study the effects of job creation schemes, wage subsidies, short-and long-term training measures and measures to promote the participation in vocational training. The outcome variables of interest are the probability to be in regular employment, and participation in further education during the 60 months following program entry. The analysis shows that all programs, except job creation schemes have positive and long-term effects on the employment probability of youth. In the short-run only short-term training measures generate positive effects, as long-term training programs and wage subsidies exhibit significant locking-in'' effects. Measures to promote vocational training are found to increase the probability of attending education and training significantly, whereas all other programs have either no or a negative effect on training participation. Effect heterogeneity with respect to the pre-treatment level education shows that young people with higher pre-treatment educational levels benefit more from participation most programs. However, for longer-term wage subsidies we also find strong positive effects for young people with low initial education levels. The relative benefit of training measures is higher in West than in East Germany. In the evaluation studies of Chapters 2 and 3 semi-parametric balancing methods of Propensity Score Matching (PSM) and Inverse Probability Weighting (IPW) are used to eliminate the effects of counfounding factors that influence both the treatment participation as well as the outcome variable of interest, and to establish a causal relation between program participation and outcome differences. While PSM and IPW are intuitive and methodologically attractive as they do not require parametric assumptions, the practical implementation may become quite challenging due to their sensitivity to various data features. Given the importance of these methods in the evaluation literature, and the vast number of recent methodological contributions in this field, Chapter 4 aims to reduce the knowledge gap between the methodological and applied literature by summarizing new findings of the empirical and statistical literature and practical guidelines for future applied research. In contrast to previous publications this study does not only focus on the estimation of causal effects, but stresses that the balancing challenge can and should be discussed independent of question of causal identification of treatment effects on most empirical applications. Following a brief outline of the practical implementation steps required for PSM and IPW, these steps are presented in detail chronologically, outlining practical advice for each step. Subsequently, the topics of effect estimation, inference, sensitivity analysis and the combination with parametric estimation methods are discussed. Finally, new extensions of the methodology and avenues for future research are presented. / In Kapitel 1 der Dissertation wird die Rolle von sozialen Netzwerken als Determinante im Suchverhalten von Arbeitslosen analysiert. Basierend auf der Hypothese, dass Arbeitslose durch ihr soziales Netzwerk Informationen über Stellenangebote generieren, sollten Personen mit großen sozialen Netzwerken eine erhöhte Produktivität ihrer informellen Suche erfahren, und ihre Suche in formellen Kanälen reduzieren. Durch die höhere Produktivität der Suche sollte für diese Personen zudem der Reservationslohn steigen. Die modelltheoretischen Vorhersagen werden empirisch getestet, wobei die Netzwerkinformationen durch die Anzahl guter Freunde, sowie Kontakthäufigkeit zu früheren Kollegen approximiert wird. Die Ergebnisse zeigen, dass das Suchverhalten der Arbeitslosen durch das Vorhandensein sozialer Kontakte signifikant beeinflusst wird. Insbesondere sinkt mit der Netzwerkgröße formelle Arbeitssuche - die Substitution ist besonders ausgeprägt für passive formelle Suchmethoden, d.h. Informationsquellen die eher unspezifische Arten von Jobangeboten bei niedrigen relativen Kosten erzeugen. Im Einklang mit den Vorhersagen des theoretischen Modells finden sich auch deutlich positive Auswirkungen einer Erhöhung der Netzwerkgröße auf den Reservationslohn. Kapitel 2 befasst sich mit den Arbeitsmarkteffekten von Vermittlungsangeboten (VI) in der frühzeitigen Aktivierungsphase von Arbeitslosen. Die Nutzung von VI könnte dabei eine „doppelte Dividende“ versprechen. Zum einen reduziert die frühe Aktivierung die Dauer der Arbeitslosigkeit, und somit auch die Notwendigkeit späterer Teilnahme in Arbeitsmarktprogrammen (ALMP). Zum anderen ist die Aktivierung durch Information mit geringeren locking-in‘‘ Effekten verbunden als die Teilnahme in ALMP. Ziel der Analyse ist es, die Effekte von frühen VI auf die Eingliederungsgeschwindigkeit, sowie die Teilnahmewahrscheinlichkeit in ALMP zu messen. Zudem werden mögliche Effekte auf die Qualität der Beschäftigung untersucht. Die Ergebnisse zeigen, dass VI die Beschäftigungswahrscheinlichkeit signifikant erhöhen, und dass gleichzeitig die Wahrscheinlichkeit in ALMP teilzunehmen signifikant reduziert wird. Für die meisten betrachteten Subgruppen ergibt sich die langfristige Reduktion der ALMP Teilnahme als Konsequenz der schnelleren Eingliederung. Für einzelne Arbeitsmarktgruppen ergibt sich zudem eine frühe und temporare Reduktion, was darauf hinweist, dass Maßnahmen mit hohen und geringen „locking-in“ Effekten aus Sicht der Sachbearbeiter austauschbar sind, was aus Effizienzgesichtspunkten fragwürdig ist. Es wird ein geringer negativer Effekt auf die wöchentliche Stundenanzahl in der ersten abhängigen Beschäftigung nach Arbeitslosigkeit beobachtet. In Kapitel 3 werden die Langzeiteffekte von ALMP für arbeitslose Jugendliche unter 25 Jahren ermittelt. Die untersuchten ALMP sind ABM-Maßnahmen, Lohnsubventionen, kurz-und langfristige Maßnahmen der beruflichen Bildung sowie Maßnahmen zur Förderung der Teilnahme an Berufsausbildung. Ab Eintritt in die Maßnahme werden Teilnehmer und Nicht-Teilnehmer für einen Zeitraum von sechs Jahren beobachtet. Als Zielvariable wird die Wahrscheinlichkeit regulärer Beschäftigung, sowie die Teilnahme in Ausbildung untersucht. Die Ergebnisse zeigen, dass alle Programme, bis auf ABM, positive und langfristige Effekte auf die Beschäftigungswahrscheinlichkeit von Jugendlichen haben. Kurzfristig finden wir jedoch nur für kurze Trainingsmaßnahmen positive Effekte, da lange Trainingsmaßnahmen und Lohnzuschüsse mit signifikanten locking-in‘‘ Effekten verbunden sind. Maßnahmen zur Förderung der Berufsausbildung erhöhen die Wahrscheinlichkeit der Teilnahme an einer Ausbildung, während alle anderen Programme keinen oder einen negativen Effekt auf die Ausbildungsteilnahme haben. Jugendliche mit höherem Ausbildungsniveau profitieren stärker von der Programmteilnahme. Jedoch zeigen sich für längerfristige Lohnsubventionen ebenfalls starke positive Effekte für Jugendliche mit geringer Vorbildung. Der relative Nutzen von Trainingsmaßnahmen ist höher in West- als in Ostdeutschland. In den Evaluationsstudien der Kapitel 2 und 3 werden die semi-parametrischen Gewichtungsverfahren Propensity Score Matching (PSM) und Inverse Probability Weighting (IPW) verwendet, um den Einfluss verzerrender Faktoren, die sowohl die Maßnahmenteilnahme als auch die Zielvariablen beeinflussen zu beseitigen, und kausale Effekte der Programmteilahme zu ermitteln. Während PSM and IPW intuitiv und methodisch sehr attraktiv sind, stellt die Implementierung der Methoden in der Praxis jedoch oft eine große Herausforderung dar. Das Ziel von Kapitel 4 ist es daher, praktische Hinweise zur Implementierung dieser Methoden zu geben. Zu diesem Zweck werden neue Erkenntnisse der empirischen und statistischen Literatur zusammengefasst und praxisbezogene Richtlinien für die angewandte Forschung abgeleitet. Basierend auf einer theoretischen Motivation und einer Skizzierung der praktischen Implementierungsschritte von PSM und IPW werden diese Schritte chronologisch dargestellt, wobei auch auf praxisrelevante Erkenntnisse aus der methodischen Forschung eingegangen wird. Im Anschluss werden die Themen Effektschätzung, Inferenz, Sensitivitätsanalyse und die Kombination von IPW und PSM mit anderen statistischen Methoden diskutiert. Abschließend werden neue Erweiterungen der Methodik aufgeführt.

La crescente attenzione riguardo l’interconnessione tra agricoltura e aspetti ambientali così come la crescita di volatilità dei prezzi dei prodotti agricoli ha posto una nuova enfasi sull’introduzione di misure ambientali nella politiche agricole e sulla ricerca di nuovi strumenti di stabilizzazione del reddito degli agricoltori. La ricerca di questa tesi di dottorato si inserisce in questo contesto e analizza i contratti agro-ambientali, misure della Politica Agricola Comunitaria (PAC) in Unione Europea (UE), sotto una duplice prospettiva. Il primo lavoro di ricerca consiste in un’analisi degli effetti dell’adesione a tali contratti sulle scelte produttive e sulle perfomance economiche degli agricoltori in cinque Paesi dell’UE. I risultati indicano un’eterogeneità di questi effetti: in alcuni Paesi i contratti agro-ambientali sembrano essere più efficaci nel promuovere pratiche agricole sostenibili, così come in alcuni Paesi il pagamento compensativo agro-ambientale sembra non essere sufficiente a compensare la perdita di reddito dei partecipanti. Questo studio è stato condotto combinando il Propensity Score Matching con lo stimatore Difference-in-Differences. Il secondo lavoro di ricerca sviluppa una nuova proposta metodologica che incorpora il rischio in un framework di Programmazione Matematica Positiva (PMP). Il modello elaborato presenta caratteri innovativi rispetto alla letteratura sull’argomento e permette di stimare simultaneamente i prezzi ombra delle risorse, la funzione di costo non lineare dell’azienda agricola e un coefficiente di avversione al rischio specifico per ciascuna azienda. Il modello è stato applicato a tre campioni di aziende e i risultati delle stime testano la calibrazione del modello e indicano valori del coefficiente di avversione al rischio coerenti con la letteratura. Infine il modello è stato impiegato nella simulazione di diversi scenari al fine di verificare il ruolo potenziale di un contratto agro-ambientale come strumento di gestione del rischio a diversi livelli di volatilità dei prezzi agricoli. / The increasing attention to the relationship between agriculture and the environment and the rise in price volatility on agricultural markets has led to a new emphasis on agri-environmental policies as well as to a search for new risk management strategies for the farmer. The research objective of this PhD thesis is in line with this challenging context, since it provides an analysis of the EU agri-environmental schemes (AESs) from two viewpoints. First, an ex-post analysis aims at investigating the AESs for their traditional role as measures which encourage sustainable farming while compensating the farmer for the income foregone in five EU Member States. The effects of AESs participation on farmer’s production plans and economic performances differs widely across Member States and in some of them the environmental payment is not enough to compensate the income foregone of participants. This study has been performed by applying a semi-parametric technique which combines a Difference-in-Differences estimator with a Propensity Score Matching estimator. The second piece of research develops a new methodological proposal to incorporate risk into a farm level Positive Mathematical Programming (PMP) model. The model presents some innovations with respect to the previous literature and estimates simultaneously the resource shadow prices, the farm non-linear cost function and a farm-specific coefficient of absolute risk aversion. The proposed model has been applied to three farm samples and the estimation results confirm the calibration ability of the model and show values for risk aversion coefficients consistent with the literature. Finally different scenarios have been simulated to test the potential role of an AES as risk management tool under different scenarios of crop price volatility.

在觀察性研究或非隨機試驗研究中，欲探討因果效應時，研究者需要重新對觀察性研究進行設計，設計目的在於重新建立一個隨機指派受試者的機制，使其得以近似一個隨機試驗研究，這樣的研究一般稱為「類隨機試驗研究」(quasi-randomized-experiments)。 傾向分數分析即為一種設計觀察性研究的方法，在不牽涉到反應變數結果之下進行設計。本文於一個病例對照研究(case-control study)中使用傾向分數進行配對接著再進一步估計處理效果，傾向分數配對是可降低觀察性研究中的選擇性偏誤的方法，透過配對可減少實驗組與對照組間的系統性差異，使研究群體在所觀察到的控制變數分配達到相似，進而得到處理效果(treatment effect)的不偏估計，為近年廣受流行病學、經濟學以及社會學領域使用的方法之一。傾向分數本身為一個條件機率，定義為研究受試者在其所觀察到的控制變數之下，接受某處理或被指派至某特定群體的機率，估計傾向分數最常見的方法為羅吉斯迴歸。 此外，自1970年代起，配對方法(matching method)開始被使用來選取合適的實驗組與對照組並進行兩群體的比較，其中「確切配對」屬於最常使用的配對方法，過去文獻中經常可見各種配對方法的結合使用，因此，本文電腦模擬研究部份，欲比較四種情境之下「傾向分數配對」與「確切配對」結合使用的效果，分別以偏誤降低比例、信賴區間覆蓋率、均方誤衡量兩種配對方法結合使用的適合情境。結果顯示若對「與處理指派中度相關的變數」且「與反應變數高度相關的變數」，其效果最為明顯。根據結果，我們總結認為「確切配對與傾向分數配對合併使用」確實會有較好的表現，但表現的好壞也取決於確切配對的變數。實證研究部份，探討家庭結構對青少年偏差行為之影響，欲了解來自非完整家庭之青少年是否較來自完整家庭之青少年更有容易出現偏差行為。 / In observational or nonrandomized studies, treatments are not randomly assigned so that baseline differences between treated and control groups are typically observed. Without properly executed, the differences would bias the treatment effect estimates. There has been a long history of using matching to eliminate confounder bias, and inferences are made based on the matched observations. The theoretical basis for matching has been developed since 1970, and among those matching methods commonly in use, the exact matching is probably the most popular one. On the other hand, introduced by Rosenbuam and Rubin in 1983, propensity scores, the conditional probability of being exposed or treated given the observed covariates, has been a welcome alternative used to adjust for baseline differences between study groups of late. Instead of matching a treated with an untreated subject by their covariates, subjects in both treated and control groups are matched by their propensity scores. In this study, we explore the benefits of using propensity score matching together with the exact matching for adjusting for baseline differences through Monte Carlo simulations. An empirical study is also be provided for illustration.

Submitted by isabela.moljf@hotmail.com (isabela.moljf@hotmail.com) on 2016-08-10T12:20:58Z No. of bitstreams: 1 luiscarlosfalcaocorreia.pdf: 1354537 bytes, checksum: 9923b5e459d1530ba2234ee5be8348b0 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2016-08-10T13:03:22Z (GMT) No. of bitstreams: 1 luiscarlosfalcaocorreia.pdf: 1354537 bytes, checksum: 9923b5e459d1530ba2234ee5be8348b0 (MD5) / Made available in DSpace on 2016-08-10T13:03:22Z (GMT). No. of bitstreams: 1 luiscarlosfalcaocorreia.pdf: 1354537 bytes, checksum: 9923b5e459d1530ba2234ee5be8348b0 (MD5) Previous issue date: 2016-05-10 / O objetivo desta dissertação é compreender os efeitos do Programa Bolsa Família (PBF) sobre o mercado de trabalho de seus beneficiários e analisar alguns de seus possíveis efeitos adversos. O PBF é uma transferência de renda destinada às famílias de baixa renda com o intuito de aliviar a condição de pobreza extrema e gerar capital humano por meio das condicionalidades. Utilizou-se nas análises um painel longitudinal de dados provenientes das duas rodadas da Pesquisas de Avaliação de Impacto do Programa Bolsa Família (AIBF I e II, conduzidas nos anos de 2005 e 2009, respectivamente) realizada por contratação do Ministério de Desenvolvimento Social e Combate à Fome (MDS). A metodologia empregada teve como base o método das diferenças em diferenças concomitante ao pareamento por escore de propensão. Os resultados empíricos obtidos ajudam a refutar a hipótese do “efeito preguiça”, demonstram um incentivo à sub-declaração da renda dos beneficiários; um aumento da informalidade laboral e um desincentivo à procura por trabalho dos indivíduos beneficiários. / The aim of this dissertation is to understand the effects of the Bolsa Familia Programme (PBF) over the labor market of its beneficiaries and analyze its likely adverse effects. The PBF is a conditional cash transfer designated to low income household in order to alleviate extreme poverty and generate human capital through its conditionalities. It was used a longitudinal panel data made of first and second PBF impact evaluation surveys (held in 2005 and 2009, respectively) performed by hiring of the Ministry of Social Development and Fight against Hunger (MDS). The methodology applied was the difference in differences combined with the propensity score matching. The empirical results obtained, help to disprove the hypothesis of "laziness effect", demonstrate an incentive to under-reporting of income of the beneficiaries; an increase in labor informality and a disincentive to looking for new jobs for the beneficiary individuals.

Propensity score methods are increasingly being used to reduce the effect of measured confounders in observational research. In medicine, censored time-to-event data is common. Using Monte Carlo simulations, this thesis evaluates the performance of nearest neighbour matching (NNM) and inverse probability of treatment weighting (IPTW) in combination with Cox proportional hazards models for estimating marginal hazard ratios. Focus is on the performance for different sample sizes and censoring rates, aspects which have not been fully investigated in this context before. The results show that, in the absence of censoring, both methods can reduce bias substantially. IPTW consistently had better performance in terms of bias and MSE compared to NNM. For the smallest examined sample size with 60 subjects, the use of IPTW led to estimates with bias below 15 %. Since the data were generated using a conditional parametrisation, the estimation of univariate models violates the proportional hazards assumption. As a result, censoring the data led to an increase in bias.