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Possibility of using overrule to evaluate overlap in causal inference: What is the performance of overrule in identifying overlap for different types of datasets?

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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Machine Learning and Causal Inference for the estimation of the effect of tacrolimus on kidney rejections

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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Effectiveness of propensity score methods with density estimation in identifying overlap for causal inference

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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Using Nearest-Neighbors to Evaluate Overlap in Causal Inference

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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Empirical Investigation of Learning Curves: Assessing Convexity Characteristics

Gogora, Kristián (author)

Nonconvexity in learning curves is almost always undesirable. A machine learning model with a non-convex 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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Non-Monotonicity in Empirical Learning Curves: Identifying non-monotonicity through slope approximations on discrete points

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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A Comparative Analysis of Learning Curve Models and their Applicability in Different Scenarios: Finding datasets patterns which lead to certain parametric curve model

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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An Empirical Study of Adaptive Kernel Density Estimation in Detecting Distributional Overlap

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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”How Much Data is Enough?” Learning curves for machine learning: Investigating alternatives to the Levenberg-Marquardt algorithm for learning curve extrapolation

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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Estimating the effect of ‘diverse’ team compositions on Dota 2 game outcomes using Inverse Probability Weighting

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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Understanding Risk Extrapolation (REx) and when it finds Invariant Relationships

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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Group Distributionally Robust Optimization for Solving Out-Of-Domain Generalization and Finding Causal Invariant Relationships

Guan, Zenan (author)

Out-of-Domain (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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Causal Inference in DotA 2 when estimated through randomized data

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 built-in 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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Validating the win-rate of heroes in Dota 2 using instrumental variable estimation

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 win-rates of a couple heroes are validated and reviewed to test the effectiveness and...

bachelor thesis 2022

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Evaluating the Performance of the Model Selection with Average ECE and Naive Calibration in Out-of-Domain Generalization Problems for Binary Classifiers

Liu, Anxian (author)

Out-of-domain (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 multi-domain calibration is one of these methods. Model selection with the...

bachelor thesis 2022

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Finding the causal effect of picking Slark in the game of Dota 2 on the final match outcome based on in-match events using the front-door adjustment

Liang, Hendy (author)

The front-door 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 front-door criterion. This involves having a mediator through which all the causal effect flows from treatment to outcome. The front-door adjustment adjusts for confounders...

bachelor thesis 2022

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Discovering the effect of hero choice on the outcome of a Dota 2 game

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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Can Invariant Risk Minimization resist the temptation of learning spurious correlations?

van Lith, Jochem (author)

Learning algorithms can perform poorly in unseen environments when they learn<br/>spurious correlations. This is known as the out-of-domain (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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An empirical study of the effects of unconfoundedness on the performance of Propensity Score Matching

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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Honesty in Causal Forests, is it worth it ?

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