The effect of cardiovascular risk on disease progression in de novo Parkinson's disease patients: An observational analysis

Background: Currently available treatment options for Parkinson's disease are symptomatic and do not alter the course of the disease. Recent studies have raised the possibility that cardiovascular risk management may slow the progression of the disease. Objectives: We estimated the effect of baseline cardiovascular risk factors on the...

Energy Efficiency Valuation: Estimating the increase in expected transaction price due to improved energy efficiency in the Dutch housing market

Recent advancements in causal inference and machine learning research have brought forward methods to estimate effects of interventions from observational data. The augmented inverse probability weighted (AIPW) estimator is such a method, which can be used to obtain estimates of potential outcomes. Potential outcomes are defined as a...

CHIME: Causal Human-In-the-Loop Model Explanations

Explaining the behaviour of Artificial Intelligence models has become a necessity. Their opaqueness and fragility are not tolerable in high-stakes domains especially. <br/>Although considerable progress is being made in the field of Explainable Artificial Intelligence, scholars have demonstrated limits and flaws of existing approaches:...

Causal Inference in DotA 2 when estimated through randomized data

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...

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

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...

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

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...

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

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....

An empirical study of the effects of unconfoundedness on the performance of Propensity Score Matching

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...

Honesty in Causal Forests, is it worth it ?

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...

Treatment Effect Estimation of the DragonNet under Overlap Violations

The large amounts of observational data available nowadays have sparked considerable interest in learning causal relations from such data using machine learning methods. One recent method for doing this, which provided promising results, is the DragonNet (Shi et al., 2019), which utilises neural networks in order to estimate average treatment...

Empirical study of GANITE’s robustness to hidden confounders

An empirical study is performed exploring the sensitivity to hidden confounders of GANITE, a method for Individualized Treatment Effect (ITE) estimation. Most real world datasets do not measure all confounders and thus it is important to know how crucial this is in order to obtain comparable predictions. This is explored through the removal of...

Empirical Evaluation of the Performance of CEVAE under Misspecification of the Latent Dimensionality

Causal machine learning deals with the inference of causal relationships between variables in observational datasets. <br/>For certain datasets, it is correct to assume a causal graph where information about unobserved confounders can only be obtained through noisy proxies, and CEVAE aims to address this case. <br/>The number of dimensions of...

Mortality Attributable to Long-Term Exposure to Ambient Fine Particulate Matter: Insights from the Epidemiologic Evidence for Understudied Locations

Epidemiologic cohort studies have consistently demonstrated that long-term exposure to ambient fine particles (PM_2.5) is associated with mortality. Nevertheless, extrapolating results to understudied locations may involve considerable uncertainty. To explore this issue, this review discusses the evidence for (i) the associated risk...

Application of do-calculus for the beta-robust scheduling method in a one-machine job environment

Job scheduling is the process where jobs are arranged in a specific sequence. This process has always been a crucial subject for companies in numerous industries. A company could significantly improve its business performance if the scheduling process is not performed optimally. As companies and products become more and more data-driven, new...

Causal inference with multi-state models, applied on estimating the effect of the IUI treatment timing for couples with unexplained subfertility

This thesis examines statistical methods to find the right timing of intrauterine insemination treatment relative to the start of the follow-up of the couples. Intrauterine insemination is a fertility treatment conducted by injecting refined sperm into a woman's uterus. Lots of research has been done on timing of steps within one IUI cycle....

Causal inference: An introduction

Experiments have always been the way to study what the effect is of interventions. Causal inference is an important aspect. In this thesis we gave an introduction to causal inference. We did this by giving an example that illustrates the Fundamental Problem of Causal Inference.<br/>The Fundamental Problem of Causal Inference states that it is...

Data integration strategies for bioinformatics with applications in biomarker and network discovery

Nowadays, with large amounts of data becoming available, solving biological quests is becoming more and more a data-driven activity. To support this, there is a need for tools that enable the integration of the many sources of data. This thesis presents several avenues that can be taken, showing how integration can support research in the life...