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Cheng, Shukun (author)Causal inference is a widely recognized concept in various domains, including medicine, for estimating the effect of a medication on a certain disease. During this estimation, overlap is commonly used to eliminate the error caused by other features. However, finding the real overlap region in practice is challenging due to the limited sample...bachelor thesis 2023
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Borges Carioca Moreno Rodrigues, Inês (author)Tacrolimus is an immunosuppressive drug given to kidney transplant patients. A low concentration of this drug can lead to kidney rejection, but to our knowledge no research has been done to causally connect the two. This paper investigates the causal effect of tacrolimus concentration on kidney rejection occurrence using predictive analysis and...master thesis 2023
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Tjong, Jonathan (author)For causal inference, sufficient overlap is needed. It is possible to use propensity scores with the positivity assumption to ensure overlap is present. However, positivity is not enough to properly identify the region of overlap. For this, propensity scores need to be used in combination with density estimation. This project aims to evaluate...bachelor thesis 2023
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Vincenti, Jort (author)To validate the results of a medical trial, there must be an overlap between the treatment and control groups. This implies the crucial need for good evaluation methods. This study, therefore, aimed to evaluate the overlap between causal classes using the Nearest Neighbours’ methods. Firstly, a case study was built around the common failures of...bachelor thesis 2023
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Gogora, Kristián (author)Nonconvexity in learning curves is almost always undesirable. A machine learning model with a nonconvex learning curve either requires a larger quantity of data to observe progress in its accuracy or experiences an exponential decrease of accuracy at low sample sizes, with no improvement in accuracy even when more data points are added. This...bachelor thesis 2023
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Socol, Codrin (author)Learning curves are used to shape the performance of a Machine Learning (ML) model with respect to the size of the set used for training it. It was commonly thought that adding more training samples would increase the model's accuracy (i.e., they are monotone), but recent works show that may not always be the case. In other words, some learners...bachelor thesis 2023
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Kalandadze, Anna (author)Learning curves display predictions of the chosen model’s performance for different training set sizes. They can help estimate the amount of data required to achieve a minimal error rate, thus aiding in reducing the cost of data collection. However, our understanding and knowledge of the various shapes of learning curves and their applicability...bachelor thesis 2023
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Chen, Chao (author)Given data from an observational study or a randomized experiment, the positivity assumption must hold in order to draw causal relations between the treatment and outcome. However, there is shortage of automatic tools that verify compliance with the positivity assumption. We present tools that uses adaptive and standard kernel density estimation...bachelor thesis 2023
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Negru, Lucian (author)The conducted research explores fitting algorithms for learning curves. Learning curves describe how the performance of a machine learning model changes with the size of the training input. Therefore, fitting these learning curves and extrapolating them can help determine the required data set size for any desired performance. <br/><br/>The...bachelor thesis 2023
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Goedhart, Christof (author)Commonly, when researchers are figuring out the effect of a putative cause, additional variables influence the cause and the effect. These are called confounders, and they obfuscate causal relationships. Inverse Probability Weighting is a method that can be applied to remove confounding and show a causal effect. This study aims to determine if...bachelor thesis 2022
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Hofland, Jeroen (author)Generalizing models for new unknown datasets is a common problem in machine learning. Algorithms that perform well for test instances with the same distribution as their training dataset often perform severely on new datasets with a different distribution. This problem is caused by distributional shifts between the training of the model and...bachelor thesis 2022
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Guan, Zenan (author)OutofDomain (OOD) generalization is a challenging problem in machine learning about learning a model from one or more domains and making the model perform well on an unseen domain. Empirical Risk Minimization (ERM), the standard machine learning method, suffers from learning spurious correlation in the training domain, therefore may perform...bachelor thesis 2022
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Avgousti, Stelios (author)Strategy games could be considered as an amazing playground for using Causal inference methods. The complex nature of the data and the builtin randomization help with testing causal inference in a scenario where in reality it would be hard and expensive. Randomized data in coherence with causal inference is well documented and tested, but not...bachelor thesis 2022
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van der Maas, David (author)This research provides additional insights in to when instrumental variable estimation is a proper method to use when investigating or removing causal effects in randomized experiments. this is done by using instrumental variables on the game Dota 2, in which winrates of a couple heroes are validated and reviewed to test the effectiveness and...bachelor thesis 2022
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Liu, Anxian (author)Outofdomain (OOD) generalization refers to learning a model from one or more different but related domain(s) that can be used in an unknown test domain. It is challenging for existing machine learning models. Several methods have been proposed to solve this problem, and multidomain calibration is one of these methods. Model selection with the...bachelor thesis 2022
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Liang, Hendy (author)The frontdoor adjustment is a causal inference method with which it is possible to determine the causal effect of applying a treatment given a setting which satisfies the frontdoor criterion. This involves having a mediator through which all the causal effect flows from treatment to outcome. The frontdoor adjustment adjusts for confounders...bachelor thesis 2022
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Toksoy, Noyan (author)Dota 2 is one of the most popular MOBA (Multiplayer Online Battle Arena) games being played today. A Dota 2 match is played by two teams of 5 players. The main goal of the game is to destroy the opposing team’s Ancient tower, the team that manages to do so, wins the game. An essential part of a match is the hero selection phase before it starts....bachelor thesis 2022
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van Lith, Jochem (author)Learning algorithms can perform poorly in unseen environments when they learn<br/>spurious correlations. This is known as the outofdomain (OOD) generalization problem. Invariant Risk Minimization (IRM) is a method that attempts to solve this problem by learning invariant relationships. Motivating examples as well as counterexamples have been...bachelor thesis 2022
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Erdelský, Andrej (author)The purpose of this research is to analyze the performance of Propensity Score Matching, a causal inference method for causal effect estimation. More specifically, investigate how Propensity Score Matching reacts to breaking the unconfoundedness assumption, one of its core conceptual pillars. This has been achieved by running PSM on synthetic...bachelor thesis 2022
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Havelka, Matej (author)Causal machine learning is a relatively new field which tries to find a causal relation between the treatment and the outcome, rather than a correlation between the features and the outcome. To achieve this, many different models were proposed, one of which is the causal forest. Causal forest is made up of a random forest, with a different...bachelor thesis 2022
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