This thesis examines philosophical controversies surrounding the evaluation of medical treatments, with a focus on the evidential roles of randomised trials and mechanisms in Evidence-Based Medicine. Current 'best practice' usually involves excluding non-randomised trial evidence from systematic reviews in cases where randomised trials are available for inclusion in the reviews. The first paper challenges this practice and evaluates whether adding of evidence from non-randomised trials might improve the quality and precision of some systematic reviews. The second paper compares the alleged methodological benefits of randomised trials over observational studies for investigating treatment benefits. It suggests that claims about the superiority of well-conducted randomised controlled trials over well-conducted observational studies are justified, especially when results from the two methods are contradictory. The third paper argues that postulating the unpredictability paradox in systematic reviews when no detectable empirical differences can be found requires further justification. The fourth paper examines the problem of absence causation in the context of explaining causal mechanisms and argues that a recent solution (Barros 2013) is incomplete and requires further justification. Solving the problem by describing absences as causes of 'mechanism failure' fails to take into account the effects of absences that lead to vacillating levels of mechanism functionality (i.e. differences in effectiveness or efficiency). The fifth paper criticises literature that has emphasised functioning versus 'broken' or 'non-functioning' mechanisms emphasising that many diseases result from increased or decreased mechanism function, rather than complete loss of function. Mechanistic explanations must account for differences in the effectiveness of performed functions, yet current philosophical mechanistic explanations do not achieve this. The last paper argues that the standard of evidence embodied in the ICE theory of technological function (i.e. testimonial evidence and evidence of mechanisms) is too permissive for evaluating whether the proposed functions of medical technologies have been adequately assessed and correctly ascribed. It argues that high-quality evidence from clinical studies is necessary to justify functional ascriptions to health care technologies. / <p>QC 20150312</p>

Submitted by Bernardo Andrade Lyrio Modenesi (bmodenesi@gmail.com) on 2015-07-22T21:31:40Z No. of bitstreams: 1 modenesi_thesis_v4whatseparatemen.pdf: 819403 bytes, checksum: d0a2f018fe44526173ae5b3f9e4c13a9 (MD5) / Rejected by Suzinei Teles Garcia Garcia (suzinei.garcia@fgv.br), reason: Prezado Bernardo bom dia, Nome da fundação e escola em negrito, o trabalho é em inglês vem primeiro o ABSTRACT depois o RESUMO e para aprovar o trabalho é necessário o seu orientador encaminhar e-mail para o cmcd@fgv.br com o novo Título autorizando o protocolo da versão final. Aguardo! Grata. Suzi 3799-7876 on 2015-07-23T11:30:51Z (GMT) / Submitted by Bernardo Andrade Lyrio Modenesi (bmodenesi@gmail.com) on 2015-07-23T14:56:25Z No. of bitstreams: 1 modenesi_thesis_v4.pdf: 819610 bytes, checksum: 8482a7c46b6c9bada5d33905e41147d7 (MD5) / Approved for entry into archive by Suzinei Teles Garcia Garcia (suzinei.garcia@fgv.br) on 2015-07-23T17:16:31Z (GMT) No. of bitstreams: 1 modenesi_thesis_v4.pdf: 819610 bytes, checksum: 8482a7c46b6c9bada5d33905e41147d7 (MD5) / Made available in DSpace on 2015-07-23T18:29:24Z (GMT). No. of bitstreams: 1 modenesi_thesis_v4.pdf: 819610 bytes, checksum: 8482a7c46b6c9bada5d33905e41147d7 (MD5) Previous issue date: 2015-06-25 / When estimating policy parameters, also known as treatment effects, the assignment to treatment mechanism almost always causes endogeneity and thus bias many of these policy parameters estimates. Additionally, heterogeneity in program impacts is more likely to be the norm than the exception for most social programs. In situations where these issues are present, the Marginal Treatment Effect (MTE) parameter estimation makes use of an instrument to avoid assignment bias and simultaneously to account for heterogeneous effects throughout individuals. Although this parameter is point identified in the literature, the assumptions required for identification may be strong. Given that, we use weaker assumptions in order to partially identify the MTE, i.e. to stablish a methodology for MTE bounds estimation, implementing it computationally and showing results from Monte Carlo simulations. The partial identification we perfom requires the MTE to be a monotone function over the propensity score, which is a reasonable assumption on several economics' examples, and the simulation results shows it is possible to get informative even in restricted cases where point identification is lost. Additionally, in situations where estimated bounds are not informative and the traditional point identification is lost, we suggest a more generic method to point estimate MTE using the Moore-Penrose Pseudo-Invese Matrix, achieving better results than traditional methods. / A estimação de parâmetros relevantes no contexto de intervenções políticas, também conhecidos como efeitos de tratamento, enfrenta diversos problemas práticos como o viés relacionado ao mecanismo de atribuição do status de tratamento. Adicionalmente, efeitos heterogênos na literatura de Avaliação de Impactos é mais comum que efeitos homogêneos. Em situações nas quais estes problemas estão presentes, a estimação do Efeito Marginal de Tratamento (MTE) emprega o método de variáveis instrumentais para contornar o viés de seleção ao tratamento, obtendo ao mesmo tempo efeitos heterogêneos. Mesmo que a literatura identifique pontualmente este parâmetro, as hipóteses por trás da identificação são costumeiramente restritivas. Neste trabalho pretende-se afrouxar as hipóteses vigentes na literatura de modo a obter identificação parcial do MTE, requerendo apenas monotonicidade do mesmo ao longo das diferentes propensões ao tratamento, o que é comum em diversos exemplos da literatura econômica. Simulações de Monte Carlo são performadas, resultando em limites para o MTE que se mostram informativos, mesmo em situações restritas nas quais a tradicional identificação pontual é perdida. Complementarmente, em situações quando a identificação parcial não é informativa e a tradicional ponto identificação é perdida, propomos a ponto identificação utilizando a matrix pseudo inversa de Moore-Penrose. Esta metodologia prescinde da hipótese de monotonicidade e resulta em melhores estimativas quando comparada aos métodos tradicionais.

[pt] Uma ferramenta importante na avaliação de políticas econômicas é a estimação do efeito médio de um programa ou tratamento sobre uma variável de interesse. A principal dificuldade desse cálculo deve-se µa atribuição do tratamento aos potenciais participantes geralmente não ser aleatória, causando viés de seleção quando desconsiderada. Uma maneira de resolver esse problema é supor que o econometrista observa um conjunto de características determinantes, a menos de um componente estritamente aleatório, da participação. Sob esta hipótese, conhecida como Ignorabilidade, métodos semiparamétricos de estimação foram desenvolvidos, entre os quais a imputação de valores contrafactuais e a reponderação da amostra. Ambos são consistentes e capazes de atingir, assintoticamente, o limite de eficiência semiparamétrico. Entretanto, nas amostras frequentemente disponíveis, o desempenho desses métodos nem sempre é satisfatório. O objetivo deste trabalho é estudar como a combinação das duas estratégias pode produzir estimadores com melhores propriedades em amostras pequenas. Para isto, consideramos duas formas de integrar essas abordagens, tendo como referencial teórico a literatura de estimação duplamente robusta desenvolvida por James Robins e co-autores. Analisamos suas propriedades e discutimos por que podem superar o uso isolado de cada uma das técnicas que os compõem. Finalmente, comparamos, num exercício de Monte Carlo, o desempenho desses estimadores com os de imputação e reponderação. Os resultados mostram que a combinação de estratégias pode reduzir o viés e a variância, mas isso depende da forma como é implementada. Concluímos que a escolha dos parâmetros de suavização é decisiva para o desempenho da estimação em amostras de tamanho moderado. / [en] Estimation of mean treatment effect is an important tool for evaluating economic policy. The main difficulty in this calculation is caused by nonrandom assignment of potential participants to treatment, which leads to selection bias when ignored. A solution to this problem is to suppose the econometrician observes a set of covariates that determine participation, except for a strictly random component. Under this assumption, known as Ignorability, semiparametric methods were developed, including imputation of counterfactual outcomes and sample reweighing. Both are consistent and can asymptotically achieve the semiparametric efficiency bound. However, in sample sizes commonly available, their performance is not always satisfactory. The goal of this dissertation is to study how combining these strategies can lead to better estimation in small samples. We consider two different ways of merging these methods, based on Doubly Robust inference literature developed by James Robins and his co-authors, analyze their properties and discuss why they would overcome each of their components. Finally, we compare the proposed estimators to imputation and reweighing in a Monte Carlo exercise. Results show that while combined strategies may reduce bias and variance, it depends on the way it is implemented. We conclude that the choice of smoothness parameters is critical to obtain good estimates in moderate size samples.

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This thesis consists of four independent chapters on bank capital regulation and the issue of unsolicited ratings.<p><p>The first chapter is introductory and reviews the motivation for regulating banks and credit rating agencies while providing a detailed overview of the thesis.<p><p>The second chapter uses a simultaneous equations model to analyze how banks from six G10 countries adjusted their capital to assets ratios and risk-weighted assets to assets ratio between 1988 and 1995, i.e. just after passage of the 1988 Basel Accord. The results suggest that regulatory pressure brought about by the 1988 capital standards had little effect on both ratios for weakly capitalized banks, except in the US. In addition, the relation between the capital to assets ratios and the risk-weighted assets to assets ratio appears to depend not only on the level of capitalization of banks, but also on the countries or groups of countries considered.<p><p>The third chapter provides Monte Carlo estimates of the amount of regulatory capital that EMU banks must hold for their corporate, bank, and sovereign exposures both under Basel I and the standardized approach to credit risk in Basel II. In the latter case, Monte Carlo estimates are presented for different combinations of external credit assessment institutions (ECAIs) that banks may choose to risk weight their exposures. Three main results emerge from the analysis. First, although the use of different ECAIs leads to significant differences in minimum capital requirements, these differences never exceed, on average, 10% of EMU banks’ capital requirements for corporate, bank, and sovereign exposures. Second, the standardized approach to credit risk provides a small regulatory capital incentive for banks to use several ECAIs to risk weight their exposures. Third, the minimum capital requirements for the corporate, bank, and sovereign exposures of EMU banks will be higher in Basel II than in Basel I. I also show that the incentive for banks to engage in regulatory arbitrage in the standardized approach to credit risk is limited.<p><p>The fourth and final chapter analyses the effect of soliciting a rating on the rating outcome of banks. Using a sample of Asian banks rated by Fitch Ratings, I find evidence that unsolicited ratings tend to be lower than solicited ones, after accounting for differences in observed bank characteristics. This downward bias does not seem to be explained by the fact that better-quality banks self-select into the solicited group. Rather, unsolicited ratings appear to be lower because they are based on public information. As a result, they tend to be more conservative than solicited ratings, which incorporate both public and non-public information.<p> / Doctorat en sciences économiques, Orientation économie / info:eu-repo/semantics/nonPublished