The Twin Instrument: Fertility and Human Capital Investment

Twin births are often used as an instrument to address selection of women into fertility. However recent work shows selection of women into twin birth such that, while OLS estimates tend to be downward biased, twin-IV estimates will tend to be upward biased. This is pertinent given the emerging consensus that fertility has limited impacts on women’s labour supply, or on investments in children. Using data for developing countries and the United States to estimate the trade-off between fertility and children’s human capital, we demonstrate the nature and size of the bias in the twin-IV estimator and estimate bounds on the true parameter

Robustness of Algorithms for Causal Structure Learning to Hyperparameter Choice

Hyperparameters play a critical role in machine learning. Hyperparameter tuning can make the difference between state-of-the-art and poor prediction performance for any algorithm, but it is particularly challenging for structure learning due to its unsupervised nature. As a result, hyperparameter tuning is often neglected in favour of using the default values provided by a particular implementation of an algorithm. While there have been numerous studies on performance evaluation of causal discovery algorithms, how hyperparameters affect individual algorithms, as well as the choice of the best algorithm for a specific problem, has not been studied in depth before. This work addresses this gap by investigating the influence of hyperparameters on causal structure learning tasks. Specifically, we perform an empirical evaluation of hyperparameter selection for some seminal learning algorithms on datasets of varying levels of complexity. We find that, while the choice of algorithm remains crucial to obtaining state-of-the-art performance, hyperparameter selection in ensemble settings strongly influences the choice of algorithm, in that a poor choice of hyperparameters can lead to analysts using algorithms which do not give state-of-the-art performance for their data.Comment: 26 pages, 16 figure

Estimating Difference-in-Differences in the Presence of Spillovers

I propose a method for difference-in-differences (DD) estimation in situations where the stable unit treatment value assumption is violated locally. This is relevant for a wide variety of cases where spillovers may occur between quasi-treatment and quasi-control areas in a (natural) experiment. A flexible methodology is described to test for such spillovers, and to consistently estimate treatment effects in their presence. This spillover-robust DD method results in two classes of estimands: treatment effects, and “close” to treatment effects. The methodology outlined describes a versatile and non-arbitrary procedure to determine the distance over which treatments propagate, where distance can be defined in many ways, including as a multi-dimensional measure. This methodology is illustrated by simulation, and by its application to estimates of the impact of state-level text-messaging bans on fatal vehicle accidents. Extending existing DD estimates, I document that reforms travel over roads, and have spillover effects in neighbouring non-affected counties. Text messaging laws appear to continue to alter driving behaviour as much as 30 km outside of affected jurisdictions

A Convenient Omitted Variable Bias Formula for Treatment Effect Models

Generally, determining the size and magnitude of the omitted variable bias (OVB) in regression models is challenging when multiple included and omitted variables are present. Here, I describe a convenient OVB formula for treatment effect models with potentially many included and omitted variables. I show that in these circumstances it is simple to infer the direction, and potentially the magnitude, of the bias. In a simple setting, this OVB is based on mutually exclusive binary variables, however I provide an extension which loosens the need for mutual exclusivity of variables, and derives the bias in difference-in-differences style models with an arbitrary number of included and excluded “treatment” indicators

Role of CD4+ T cells in the regulation of the immune response against encapsulated Group B Streptococcus

Le Streptocoque de groupe B (GBS) est un important agent d’infection invasive pouvant mener à la mort et demeure la cause principale de septicémie néonatale à ce jour. Neuf sérotypes ont été officiellement décrits basés sur la composition de la capsule polysaccharidique (CPS). Parmi ces sérotypes, le type III est considéré le plus virulent et fréquemment associé aux maladies invasives graves, telle que la méningite. Malgré que plusieurs recherches aient été effectuées au niveau des interactions entre GBS type III et les cellules du système immunitaire innées, aucune information n’est disponible sur la régulation de la réponse immunitaire adaptative dirigée contre ce dernier. Notamment, le rôle de cellules T CD4+ dans l’immuno-pathogenèse de l’infection causée par GBS n’a jamais été étudié. Dans cet étude, trois différents modèles murins d’infection ont été développé pour évaluer l’activation et la modulation des cellules T CD4+ répondantes au GBS de type III : ex vivo, in vivo, et in vitro. Les résultats d’infections ex vivo démontrent que les splénocytes totaux répondent à l’infection en produisant des cytokines de type-1 pro-inflammatoires. Une forte production d’IL-10 accompagne cette cascade inflammatoire, probablement dans l’effort de l’hôte de maintenir l’homéostasie. Les résultats démontrent aussi que les cellules T sont activement recrutées par les cellules répondantes du système inné en produisant des facteurs chimiotactiques, tels que CXCL9, CXCL10, et CCL3. Plus spécifiquement, les résultats obtenus à partir des cellules isolées T CD4+ provenant des infections ex vivo ou in vivo démontrent que ces cellules participent à la production d’IFN-γ et de TNF-α ainsi que d’IL-2, suggérant un profil d’activation Th1. Les cellules isolées T CD4+ n’étaient pas des contributeurs majeurs d’IL-10. Ceci indique que cette cytokine immuno-régulatrice est principalement produite par les cellules de l’immunité innée de la rate de souris infectées. Le profil Th1 des cellules T CD4+ a été confirmé en utilisant un modèle in vitro. Nos résultats démontrent aussi que la CPS de GBS a une role immuno-modulateur dans le développement de la réponse Th1. En résumé, cette étude adresse pour la première fois, la contribution des cellules T CD4+ dans la production d’IFN-γ lors d’une infection à GBS et donc, dans le développement d’une réponse de type Th1. Ces résultats renforcent d’avantage le rôle central de cette cytokine pour un control efficace des infections causées par ce pathogène.Group B Streptococcus (GBS) is an important agent of life-threatening invasive infections and remains the leading cause of neonatal sepsis to this day. Nine serotypes have been officially described based on capsular polysaccharide (CPS) composition. Among them, capsular type III is considered one of the most virulent and frequently associated with severe invasive diseases, such as meningitis. Although extensive research has been done on the interactions between GBS type III and various cells of the innate immune system, no information is available on the regulation of the adaptive immune response against this pathogen. In particular, the role of CD4+ T cells in the immuno-pathogenesis of the infection caused by GBS has never been assessed. In this study, three different models of murine infection were developed to evaluate activation and modulation of responding CD4+ T cells against GBS type III: ex vivo, in vivo, and in vitro. Ex vivo analysis of total splenocytes showed that GBS induces the release of type-1 pro-inflammatory cytokines. A strong IL-10 production follows this inflammatory cascade, indicating the host effort to maintain homeostasis. Results also indicate that T cells were actively recruited by responding innate immune cells via the release of chemotactic factors such as CXCL9, CXCL10, and CCL3. More specifically, results obtained from isolated CD4+ T cells from ex vivo or in vivo infections showed that they actively participate in the production of IFN-γ and TNF-α, as well as IL-2, suggesting a Th1 profile of activation. On the other hand, isolated CD4+ T cells were not main sources of IL-10. This observation suggests that this immuno-regulatory cytokine is produced mainly by cells of the spleen innate immune system of infected animals. The CD4+ Th1 cell profile was confirmed using an in vitro model of infection. Our results also suggest that the GBS CPS plays an immuno-modulatory role in the development of a Th1 response. In summary, this study addresses for this first time the contribution of CD4+ T cells in IFN-γ production during GBS infection, and thus, in the development of a Th1 response. Our data further highlight the central role of this cytokine for effective control of GBS infections

A Convenient Omitted Variable Bias Formula for Treatment Effect Models

Generally, determining the size and magnitude of the omitted variable bias (OVB) in regression models is challenging when multiple included and omitted variables are present. Here, I describe a convenient OVB formula for treatment effect models with potentially many included and omitted variables. I show that in these circumstances it is simple to infer the direction, and potentially the magnitude, of the bias. In a simple setting, this OVB is based on mutually exclusive binary variables, however I provide an extension which loosens the need for mutual exclusivity of variables, and derives the bias in difference-in-differences style models with an arbitrary number of included and excluded “treatment” indicators

(Frisch-Waugh-Lovell)': On the Estimation of Regression Models by Row

We demonstrate that regression models can be estimated by working independently in a row-wise fashion. We document a simple procedure which allows for a wide class of econometric estimators to be implemented cumulatively, where, in the limit, estimators can be produced without ever storing more than a single line of data in a computer's memory. This result is useful in understanding the mechanics of many common regression models. These procedures can be used to speed up the computation of estimates computed via OLS, IV, Ridge regression, LASSO, Elastic Net, and Non-linear models including probit and logit, with all common modes of inference. This has implications for estimation and inference with `big data', where memory constraints may imply that working with all data at once is particularly costly. We additionally show that even with moderately sized datasets, this method can reduce computation time compared with traditional estimation routines

Ensuring equitable access to high quality care: The task of uplifting trauma care in rural and district hospitals

-

Developing a multi-faceted approach to improving and uplifting trauma care in the periphery.

Doctor of Philosophy in General Surgery. University of KwaZulu-Natal, Medical School 2013.Introduction Rural trauma care in South Africa is under resourced and the quality of rural trauma care appears to be uneven. This project aimed to assess the quality of rural trauma care in Sisonke Health District and to develop targeted quality improvement programmes to improve it. Methodology A strategic planning methodology consisting of a situational analysis, planning synthesis and implementation was used in the project and was integrated with a health system’s model of inputs, process and outcome to provide a structured overview of the whole process. A number of academic constructs from fields outside of health care were used to analyse the quality of care and to develop targeted quality improvement programmes. Results The table below summarises the results of this project by placing each of the published papers in this thesis into the integrated grid. The various tools that were adopted to assist with the project included error theory and quality metrics for trauma and acute surgery. These are also situated within the grid. Analysis of the inputs of rural trauma care revealed that there were major deficits in terms of the human resources available to manage the large burden of trauma seen in rural hospitals. Analysis of the process revealed deficits in the transfer process and the quality of documentation and observation of trauma patients in our system. Analysis of the outcomes revealed a high incidence of error associated with rural trauma care and poor outcomes for a number of conditions such as burns. Synthesis and Implementation involved the development of a number of strategies and a review of their efficacy. These included a surgical outreach programme, restructured morbidity and mortality meetings, error-awareness training and the use of tick-box clerking sheets. The impact of these various programmes was mixed. The surgical outreach programme was successful at delivering surgical care in the districts but less successful at transferring surgical skills to rural staff. The morbidity and mortality meetings, and the errorawareness training changed the culture of the institution and increased the understanding of the danger of error. The tick-box initiative revealed how difficult it is to change human behaviour. A number of audits have suggested that there is a general improvement in the quality of care. This has resulted in improved outcomes for the management of penetrating abdominal trauma and burns care. Conclusion Rural trauma care has many deficits and these translate into poor outcomes. Addressing these deficits is difficult and requires a multi -faceted approach. Undertaking quality improvement programmes in an ad hoc manner may be counter-productive and using a structured systematic approach may allow planners to contextualise their interventions. Currently trying to increase the inputs and resources available for rural trauma care is difficult and most of the intervention should aim at refining and improving the process of care. A number of projects have emerged from this thesis