My thesis titled "Microeconomic Impact of Structural Funds in the Nonprofit Sector" focuses on the analysis of the impact of EU funds on the capacities of Czech NGOs. I dealt with the situation of public benefit companies. I have measured the impact on capacity through economic indicators derived from financial statements of organizations which are the total revenues, total staff costs, profit and total debt. Results of the analysis has been achieved by comparison of supported and unsupported organizations for the period 2006-2011 using selected methods of counterfactual impact evaluation as the difference-in-difference method and matching method. Using these methods I have been able to find a positive effect of EU Structural funds on the capacities of Czech non-profit organizations.

This paper is dedicated to the empirical analysis of the pilot trainee project Stáže ve firmách, which is considered as treatment in this analysis. The main objective of the empirical analysis is estimation of average treatment effect(ATE) and average treatment effect on treated(ATET) for characteristics like socioeconomic status and wage. Counterfactual methods for policy impact evaluation like Difference in Differences Estimator(DiD), First Differences Estimator(FD) and Propensity Score Matching(PSM) are used to estimation mentioned effects. This paper contains extension of Assignment Problem that is used for people matching purposes as alternative for PSM. This way of matching provides better control over creation of couples. Resulting pairs are more similar in selected characteristics due to better control during couples creation process.

Educational assortative mating and earnings inequality has both increased in both Europe and the United States in the last decades. As a result, educational assortative mating, or educational homogamy, has been suggested as a potential explanation for the increase in earnings inequality. According to this hypothesis increased sorting on education will lead to polarization between lower and higher-educated couples where the advantages of the latter will compound on one another and lead to increased economic inequality.   The majority of the studies to date report a non-relationship between educational assortative mating and earnings inequality, one of the exceptions being a study of Denmark. This exception has led sociologists to theorize that the impact of educational assortative mating could be especially strong in the Nordic countries. In this study I test this hypothesis by employing a novel decomposition method, the Theil-index, to answer if increases in educational assortative mating are associated with increases in earnings inequality in Sweden between 2000 and 2010, using data from the Standard of Living Survey (LNU).   The result is a non-relationship between homogamy and earnings inequality and an overall decrease in earnings inequality in the sample. The result is another null result for the hypothesis that educational homogamy leads to inequality, and points to a larger discrepancy between singles and couples than between couples. If corroborated, this decrease in earnings inequality would mean a divergence, in earnings inequality, between partnered individuals and the general population. Future studies should focus on the extent of this divergence.

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This thesis examines the effectiveness of macroprudential policies on reducing housing price growth in the international database of 56 countries with the use of GMM and fixed effects between 2000 and 2017. The macroprudential index is added to the dynamic panel data model where the housing price index is regressed on housing price determinants as the economic growth or unemployment rate. The analysis is also conducted on the sample of countries with a higher market share of owners with a mortgage as there is a higher opportunity to control the housing market through the credit channel. Nevertheless, results show that we do not have enough evidence to state that macroprudential policies curb house price booms. Contrarily, the effect seems to work in the opposite direction which is probably caused by a reverse causality between the growth of real estate prices and the implementation of macroprudential tools. The debt-to-income restriction is the only tool that decreases housing price growth according to the fixed effects model. Detailed counterfactual analysis of the Czech market proposes only a slight impact of the loan-to-value measure on the apartment price development according to one out of four predictions. 1

[pt] Nas duas últimas décadas, o nível de reservas internacionais nos países emergentes aumentou de forma significativa. No Brasil, o nível de 2019 de 360 bilhões de dólares era considerado alto por algumas métricas, com base no motivo precaucional. Por outro lado, além do custo de oportunidade, a América Latina também se caracteriza pelo custo historicamente alto de carregamento das reservas, devido ao pagamento de juros positivos e altos. Por trás de qualquer modelo na literatura que estuda que estuda o nível ótimo das reservas, existe uma ponderação entre os benefícios e os custos associados à acumulação das reservas, de forma que é esperado queuma mudança significativa nesta taxa seja relevante na otimização feita pelo Banco Central. Nesse sentido, recentemente, a taxa de juros alvo da política monetária no Brasil (Selic) caiu consideravelmente, de 14.25 porcento até outubro de 2016 para 2.25 porcento em junho de 2020. Com relação a esta questão, este trabalho estuda o efeito desta mudança na direção da política monetária brasileira na gestão de reservas cambiais. Nossos resultados contrafactuais mostram que o nível de reservas líquido - referência adotada pelo Banco Central desde Agosto de 2019 - teria caído neste período, controlando pelo efeito de outros determinantes ao nível ótimo de reservas, mas a queda na taxa de juros tornou possível que o Banco Central mantivesse um nível aproximadamente estável. / [en] Over the past two decades, the level of international reserves in emerging economies increased significantly. In Brazil, the 2019 level of around 360 billion dollars was considered high by some metrics, based on the precautionary motive. On the other hand, in addition to the opportunity cost, Latin America is also characterized by historically high costs of holding reserves, due to the payment of positive and high interest rates. Behind any model in the literature that studies the optimal level of reserves, there is a trade-off between the insurance benefits and the costs associated with the accumulation of reserves, so that a significant change in this rate is expected to be relevant in the optimization made by the Central Bank. In that sense, recently, the policy-related interest rate in Brazil (Selic) decreased considerably, from 14.25 percent until October 2016 to 2.25 percent until June 2020, an all-time low. Addressing this issue, this thesis studies the effect of this change in the direction of Brazil s monetary policy in the management of foreign exchange reserves. Our counterfactual results show that the net FX reserves level - a benchmark adopted by the Central Bank since August 2019 -, would have fallen in this period, but the decline in the interest rates made it possible for the Central Bank to keep a roughly stable level until 2019.

Artificial Intelligence (AI) has emerged as a transformative force in wireless communications, driving innovation to address the complex challenges faced by communication systems. In this context, the optimization of limited radio resources plays a crucial role, and one important aspect is the Modulation and Coding Scheme (MCS) selection. AI solutions for MCS selection have been predominantly characterized as black-box models, which suffer from limited explainability and consequently hinder trust in these algorithms. Moreover, the majority of existing research primarily emphasizes enhancing explainability without concurrently improving the model’s performance which makes performance and explainability a trade-off. This work aims to address these issues by employing eXplainable AI (XAI), particularly counterfactual and causal analysis, to increase the explainability and trustworthiness of black-box models. We propose CounterFactual Retrain (CF-Retrain), the first method that utilizes counterfactual explanations to improve model performance and make the process of performance enhancement more explainable. Additionally, we conduct a causal analysis and compare the results with those obtained from an analysis based on the SHapley Additive exPlanations (SHAP) value feature importance. This comparison leads to the proposal of novel hypotheses and insights for model optimization in future research. Our results show that employing CF-Retrain can reduce the Mean Absolute Error (MAE) of the black-box model by 4% while utilizing only 14% of the training data. Moreover, increasing the amount of training data yields even more pronounced improvements in MAE, providing a certain level of explainability. This performance enhancement is comparable to or even superior to using a more complex model. Furthermore, by introducing causal analysis to the mainstream SHAP value feature importance, we provide a novel hypothesis and explanation of feature importance based on causal analysis. This approach can serve as an evaluation criterion for assessing the model’s performance. / Artificiell intelligens (AI) har dykt upp som en transformativ kraft inom trådlös kommunikation, vilket driver innovation för att möta de komplexa utmaningar som kommunikationssystem står inför. I detta sammanhang spelar optimeringen av begränsade radioresurser en avgörande roll, och en viktig aspekt är valet av Modulation and Coding Scheme (MCS). AI-lösningar för val av modulering och kodningsschema har övervägande karaktäriserats som black-box-modeller, som lider av begränsad tolkningsbarhet och följaktligen hindrar förtroendet för dessa algoritmer. Dessutom betonar majoriteten av befintlig forskning i första hand att förbättra förklaringsbarheten utan att samtidigt förbättra modellens prestanda, vilket gör prestanda och tolkningsbarhet till en kompromiss. Detta arbete syftar till att ta itu med dessa problem genom att använda XAI, särskilt kontrafaktisk och kausal analys, för att öka tolkningsbarheten och pålitligheten hos svarta-box-modeller. Vi föreslår CF-Retrain, den första metoden som använder kontrafaktiska förklaringar för att förbättra modellens prestanda och göra processen med prestandaförbättring mer tolkningsbar. Dessutom gör vi en orsaksanalys och jämför resultaten med de som erhålls från en analys baserad på värdeegenskapens betydelse. Denna jämförelse leder till förslaget av nya hypoteser och insikter för modelloptimering i framtida forskning. Våra resultat visar att användning av CF-Retrain kan minska det genomsnittliga absoluta felet för black-box-modellen med 4% samtidigt som man använder endast 14% av träningsdata. Dessutom ger en ökning av mängden träningsdata ännu mer uttalade förbättringar av Mean Absolute Error (MAE), vilket ger en viss grad av tolkningsbarhet. Denna prestandaförbättring är jämförbar med eller till och med överlägsen att använda en mer komplex modell. Dessutom, genom att introducera kausal analys till de vanliga Shapley-tillsatsförklaringarna värdesätter egenskapens betydelse, ger vi en ny hypotes och tolkning av egenskapens betydelse baserad på kausalanalys. Detta tillvägagångssätt kan fungera som ett utvärderingskriterium för att bedöma modellens prestanda.

The aim of this thesis is to develop and explore models in, and related to, the sufficient cause framework, and additive interaction. Additive interaction is closely connected with public health interventions and can be used to make inferences about the sufficient causes in order to find the mechanisms behind an outcome, for instance a disease. In paper A we extend the additive interaction, and interventions, to include continuous exposures. We show that there does not exist a model that does not lead to inconsistent conclusions about the interaction. The sufficient cause framework can also be expressed using Boolean functions, which is expanded upon in paper B. In this paper we define a new model based on the multifactor potential outcome model (MFPO) and independence of causal influence models (ICI). In paper C we discuss the modeling and estimation of additive interaction in relation to if the exposures are harmful or protective conditioned on some other exposure. If there is uncertainty about the effects direction there can be errors in the testing of the interaction effect. / Målet med denna avhandling är att utveckla, och utforska modeller i det så kallade sufficent cause ramverket, och additiv interaktion. Additiv interaktion är nära kopplat till interventioner inom epidemiology och sociologi, men kan också användas för statistiska tester för sufficient causes för att förstå mekanimser bakom ett utfall, tex en sjukdom. I artikel A så expanderar vi modellen för additiv interaktion och interventioner till att också inkludera kontinuerliga variabler. Vi visar att det inte finns någon modell som inte leder till motsägelser i slutsatsen om interaktionen. Sufficient cause ramverket kan också utryckas via Boolska funktioner, vilket byggs vidare på i artikel B. I den artikeln definerar vi en modell baserad på mutltifactor potential outcome modellen (MFPO) och independence of causal influence modellen (ICI). I artikel C diskuterar vi modelleringen och estimering av additiv interaktion i relation till om variablerna har skadlig eller skyddande effekt betingat på någon annan variabel. Om det finns osäkerhet kring en effekts riktning så kan det leda till fel i testerna för den additiva interaktionen. / <p>Examinator: Professor Henrik Hult, Matematik, KTH</p>

<p dir="ltr">Learning, planning, and representing knowledge at multiple levels of temporal abstractions provide an agent with the ability to predict consequences of different courses of actions, which is essential for improving the performance of sequential decision making. However, discovering effective temporal abstractions, which the agent can use as skills, and adopting the constructed temporal abstractions for efficient policy learning can be challenging. Despite significant advancements in single-agent settings, temporal abstractions in multi-agent systems remains underexplored. This thesis addresses this research gap by introducing novel algorithms for discovering and employing temporal abstractions in both cooperative and competitive multi-agent environments. We first develop an unsupervised spectral-analysis-based discovery algorithm, aiming at finding temporal abstractions that can enhance the joint exploration of agents in complex, unknown environments for goal-achieving tasks. Subsequently, we propose a variational method that is applicable for a broader range of collaborative multi-agent tasks. This method unifies dynamic grouping and automatic multi-agent temporal abstraction discovery, and can be seamlessly integrated into the commonly-used multi-agent reinforcement learning algorithms. Further, for competitive multi-agent zero-sum games, we develop an algorithm based on Counterfactual Regret Minimization, which enables agents to form and utilize strategic abstractions akin to routine moves in chess during strategy learning, supported by solid theoretical and empirical analyses. Collectively, these contributions not only advance the understanding of multi-agent temporal abstractions but also present practical algorithms for intricate multi-agent challenges, including control, planning, and decision-making in complex scenarios.</p>

This thesis delves into the use of Artificial Intelligence (AI) for quality control in manufacturing systems, with a particular focus on anomaly detection through the analysis of torque measurements in rotating mechanical systems. The research specifically examines the effectiveness of torque measurements in quality control of locks, challenging the traditional method that relies on human tactile sense for detecting mechanical anomalies. This conventional approach, while widely used, has been found to yield inconsistent results and poses physical strain on operators. A key aspect of this study involves conducting experiments on locks using torque measurements to identify mechanical anomalies. This method represents a shift from the subjective and physically demanding practice of manually testing each lock. The research aims to demonstrate that an automated, AI-driven approach can offer more consistent and reliable results, thereby improving overall product quality. The development of a machine learning model for this purpose starts with the collection of training data, a process that can be costly and disruptive to normal workflow. Therefore, this thesis also investigates strategies for predicting and minimizing the sample size used for training. Additionally, it addresses the critical need of trustworthiness in AI systems used for final quality control. The research explores how to utilize machine learning models that are not only effective in detecting anomalies but also offers a level of interpretability, avoiding the pitfalls of black box AI models. Overall, this thesis contributes to advancing automated quality control by exploring the state-of-the-art machine learning algorithms for mechanical fault detection, focusing on sample size prediction and minimization and also model interpretability. To the best of the author’s knowledge, it is the first study that evaluates an AI-driven solution for quality control of mechanical locks, marking an innovation in the field. / Denna avhandling fördjupar sig i användningen av Artificiell Intelligens (AI) för kvalitetskontroll i tillverkningssystem, med särskilt fokus på anomalidetektion genom analys av momentmätningar i roterande mekaniska system. Forskningen undersöker specifikt effektiviteten av momentmätningar för kvalitetskontroll av lås, vilket utmanar den traditionella metoden som förlitar sig på människans taktila sinne för att upptäcka mekaniska anomalier. Denna konventionella metod, som är brett använd, har visat sig ge inkonsekventa resultat och medför fysisk belastning för operatörerna. En nyckelaspekt av denna studie innebär att genomföra experiment på lås med hjälp av momentmätningar för att identifiera mekaniska anomalier. Denna metod representerar en övergång från den subjektiva och fysiskt krävande praxisen att manuellt testa varje lås. Forskningen syftar till att demonstrera att en automatiserad, AI-driven metod kan erbjuda mer konsekventa och tillförlitliga resultat, och därmed förbättra den övergripande produktkvaliteten. Utvecklingen av en maskininlärningsmodell för detta ändamål börjar med insamling av träningsdata, en process som kan vara kostsam och störande för det normala arbetsflödet. Därför undersöker denna avhandling också strategier för att förutsäga och minimera mängden av data som används för träning. Dessutom adresseras det kritiska behovet av tillförlitlighet i AI-system som används för slutlig kvalitetskontroll. Forskningen utforskar hur man kan använda maskininlärningsmodeller som inte bara är effektiva för att upptäcka anomalier, utan också erbjuder en nivå av tolkningsbarhet, för att undvika fallgroparna med svart låda AI-modeller. Sammantaget bidrar denna avhandling till att främja automatiserad kvalitetskontroll genom att utforska de senaste maskininlärningsalgoritmerna för detektion av mekaniska fel, med fokus på prediktion och minimering av mängden träningsdata samt tolkbarheten av modellens beslut. Denna avhandling utgör det första försöket att utvärdera en AI-driven strategi för kvalitetskontroll av mekaniska lås, vilket utgör en nyskapande innovation inom området.