Data-Driven Optimization to Learn Structural Models

The rapid accumulation of high-dimensional data has opened new opportunities to make informed decisions. In this thesis, we focus on estimation of structural models from observational data using optimization and statistics to understand the effects of strategic decisions. We develop efficient procedures that blend techniques from economic modeling and machine learning to uncover underlying models efficiently and accurately.In Chapter 2, we focus on understanding the effect of performance-based incentives on worker performance using historical contract data. The design of performance-based incentives can be naturally posed as a moral hazard principal-agent problem. In this setting, a key input to the principal’s optimal contracting problem is the agent’s production function – the dependence of agent output on effort. While agent production is classically assumed to be known to the principal, this is unlikely to be the case in practice. Motivated by the design of performance-based incentives, we present a method for estimating a principal-agent model from data on incentive contracts and associated outcomes, with a focus on estimating agent production. The proposed estimator is statistically consistent and can be expressed as a mathematical program. To circumvent computational challenges with solving the estimation problem exactly, we approximate it as an integer program, which we solve through a column generation algorithm that uses hypothesis tests to select variables. We show that our approximation scheme and solution technique both preserve the estimator’s consistency and combine to dramatically reduce the computational time required to obtain sound estimates. To demonstrate our method, we conducted an experiment on a crowdwork platform (Amazon Mechanical Turk) by randomly assigning incentive contracts with varying pay rates among a pool of workers completing the same task. We present numerical results illustrating how our estimator combined with experimentation can shed light on the efficacy of performance-based incentives.In Chapter 3, we focus on learning causal structures from observational data, a process known as causal discovery. We propose a new optimization-based method for causal discovery. Our method takes as input observational data over a set of variables and returns a graph in which causal relations are specified by directed edges. We consider a highly general search space that accommodates latent confounders and feedback cycles, which few extant methods do. We formulate the discovery problem as an integer program, and propose a solution technique that leverages the conditional independence structure in the data to identify promising edges for inclusion in the output graph. Our method is among the very first to bring integer programming to general causal discovery, which we believe is one of our main contributions. In the large-sample limit, our method recovers a graph that is equivalent to the true data-generating graph. Computationally, our method is competitive with the state-of-the-art, and can solve in minutes instances that are intractable for alternative causal discovery methods. We then extend our framework to a priori identify a subset of variables that collectively carry all useful information about the variable of interest. This way, we can sidestep the computational burden of learning causal relations among variables of secondary importance.In Chapter 4, we focus on investigating the validity of instrumental variables, which are widely used to estimate causal effects in the presence of unmeasured confounding. In particular, we apply our method developed in Chapter 3 to US Census data from the seminal paper on the returns to education by (Angrist and Krueger, 1991), which contains a pioneering application of an instrumental variable, but one whose validity has been contested. We find that the causal structures uncovered by our method are consistent with the literature on the instrument from (Angrist and Krueger, 1991), and that our method pinpoints some of the sources of debate. Our results suggest that our graphical approach can be a useful complement to well-established empirical methods

Birden fazla çıktının birden fazla kullanıcıya dağılımlarına karşılık gelen alternatifler arasında eşitlikçi karar verme yöntemleri

Cataloged from PDF version of article.Thesis (M.S.): Bilkent University, Department of Industrial Engineering, İhsan Doğramacı Bilkent University, 2017.Includes bibliographical references (leaves 64-69).In this study, we develop decision support tools for policy makers that will help them make choices among a set of allocation alternatives. We assume that alternatives are evaluated based on their benefits to different users and that there are multiple benefit (output) types to consider. We assume that the policy maker has both efficiency (maximizing total output) and equity (distributing outputs across different users as fair as possible) concerns. This problem is a multicriteria decision making problem where the alternatives are represented with matrices rather than vectors. We develop interactive algorithms that guide a policy maker to her most preferred solution (a set of most preferred solutions), which are based on utility additive (UTA) and convex cone methods. Our computational experiments demonstrate the satisfactory performance of the algorithms. We believe that such decision support tools may be of great use in practice and help in moving towards fair and efficient allocation decisions.by Sema Nur Kaynar Keleş.M.S

Psychometric Testing of the Turkish Version of the Technology Informatics Guiding Educational Reform-Based Assessment of Nursing Informatics Competencies Tool

Nursing informatics competencies are vital to benefit from information technologies to improve patient outcomes. It is essential to use a reliable and valid instrument for evaluating competencies. The Technology Informatics Guiding Educational Reform-Based Assessment of Nursing Informatics Competencies Tool is a valid and reliable tool used to evaluate nursing informatics competencies in nurses who primarily speak English. This cross-sectional research aimed to evaluate the psychometric properties of a Turkish version of the instrument. Data were collected from 518 nurses working in two university hospitals in Istanbul, Turkey. The tool was translated into Turkish, validated by professional experts, back-translated, and analyzed. Thirty nurses completed the tool twice for test-retest reliability. A four-factor structure identified in exploratory factor analysis (73.64% of the total variance with all items loaded >0.40 [0.44-0.88] for each factor). Cronbach's alpha reliability coefficients of the subsets were .98 for basic computer skills, .97 for clinical information management, and .98 for information literacy. The total item correlations for subsets were between 0.57 and 0.84. The Turkish version of the Technology Informatics Guiding Educational Reform-Based Assessment of Nursing Informatics Competencies demonstrated sufficient reliability and validity for assessing nursing informatics competencies within Turkish culture

Konya Yeni Meram Gazetesi'nin kuruluş ve gelişim yılları (1950-1980)

Ankara : İhsan Doğramacı Bilkent Üniversitesi İktisadi, İdari ve Sosyal Bilimler Fakültesi, Tarih Bölümü, 2013.This work is a student project of the The Department of History, Faculty of Economics, Administrative and Social Sciences, İhsan Doğramacı Bilkent University.by Elif Huntürk.Huntürk, Elif. HIST 200-8HUNTÜRK HIST 200-8/6 2012-1

A comprehensive health effects assessment of the use of sanitizers and disinfectants during COVID-19 pandemic: a global survey

COVID-19 has affected all aspects of human life so far. From the outset of the pandemic, preventing the spread of COVID-19 through the observance of health protocols, especially the use of sanitizers and disinfectants was given more attention. Despite the effectiveness of disinfection chemicals in controlling and preventing COVID-19, there are critical concerns about their adverse effects on human health. This study aims to assess the health effects of sanitizers and disinfectants on a global scale. A total of 91,056 participants from 154 countries participated in this cross-sectional study. Information on the use of sanitizers and disinfectants and health was collected using an electronic questionnaire, which was translated into 26 languages via web-based platforms. The findings of this study suggest that detergents, alcohol-based substances, and chlorinated compounds emerged as the most prevalent chemical agents compared to other sanitizers and disinfectants examined. Most frequently reported health issues include skin effects and respiratory effects. The Chi-square test showed a significant association between chlorinated compounds (sodium hypochlorite and per-chlorine) with all possible health effects under investigation (p-value <0.001). Examination of risk factors based on multivariate logistic regression analysis showed that alcohols and alcohols-based materials were associated with skin effects (OR, 1.98; 95%CI, 1.87-2.09), per-chlorine was associated with eye effects (OR, 1.83; 95%CI, 1.74-1.93), and highly likely with itching and throat irritation (OR, 2.00; 95%CI, 1.90-2.11). Furthermore, formaldehyde was associated with a higher prevalence of neurological effects (OR, 2.17; 95%CI, 1.92-2.44). Furthermore, formaldehyde was associated with a higher prevalence of neurological effects (OR, 2.17; 95%CI, 1.92-2.44). The use of sodium hypochlorite and per-chlorine also had a high chance of having respiratory effects. The findings of the current study suggest that health authorities need to implement more awareness programs about the side effects of using sanitizers and disinfectants during viral epidemics especially when they are used or overused