Revealing causal structures from observational data is an essential task in many data analysis issues across various domains, such as natural sciences, business, and healthcare. In healthcare, neuropathic pain is one of the most common medical problems, whose diagnosis process has well-understood causal structures. Causal structures are commonly visualized as a directed acyclic graph (DAG) or a node-link diagram, in which nodes represent variables, and edges represent causal relationships between data dimensions. However, these simple static graphs do not convey sufficient information for either an intuitive interpretation for novel viewers, or an in-depth exploration for experts. In this study, the visualization of causal structures for neuropathic pain diagnosis is set into context. An interactive system that integrates application-specific visualization, i.e. a discomfort drawing and a spinal cord diagram, into causality visualization is developed. It is further evaluated by a domain expert on neuropathic pain and a researcher in causal discovery through semi-structured interviews. The results show that the system reveals the causal structures for neuropathic pain diagnosis in a more intuitive, efficient way, and conveys more focused information compared to traditional node-link diagrams. The system is also demonstrated to be helpful to the medical community in neuropathic pain diagnosis, not only for doctors but also for patients. / Att upptäcka kausala strukturer från observationsdata är en viktig uppgift i många dataanalysfrågor inom olika områden, t.ex. naturvetenskap, affärsverksamhet och sjukvård. Inom hälso- och sjukvården är neuropatisk smärta ett av de vanligaste medicinska problemen. Dess diagnosprocess har väl förstådda kausala strukturer. Kausala strukturer visualiseras vanligtvis som en riktad acyklisk graf (DAG) eller nätverksdiagram, där noder representerar variabler och kanter representerar kausala relationer mellan datadimensioner. Dessa enkla statiska grafer förmedlar dock inte tillräckligt information för att ge en intuitiv tolkning för nya betraktare eller en djupgående utforskning för experter. I den här studien sätts visualiseringen av kausala strukturer för diagnostisering av neuropatisk smärta i ett sammanhang. Ett interaktivt system som integrerar applikationsspecifik visualisering, dvs. en smärtteckning och ett ryggmärgsdiagram, i kausalitetsvisualisering utvecklas. Det utvärderas av en domänexpert på neuropatisk smärta och en forskare inom kausal upptäckt genom semistrukturerade intervjuer. Resultaten visar att systemet avslöjar kausalstrukturer för diagnos av neuropatisk smärta på ett mer intuitivt och effektivt sätt och förmedlar mer fokuserad information jämfört med traditionella diagrammer. Systemet har också visat sig vara till hjälp för det medicinska samfundet vid diagnostisering av neuropatisk smärta, inte bara för läkare utan även för patienter.

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This essay argues that personal greenhouse gas emissions render the individual responsible forclimate-related harm to a great extent. To accomplish this, there will primarily be a focus onanswering the most important criticisms of individual climate responsibility. Issues concerningcausality are the first to be brought up, followed by issues concerning direct harm, simpledivision and unintentional contributions to harm, among other topics. The three main conclusionsdrawn in the discussion of these topics are that individual emissions can be considered partialcauses of climate harm, that most emission-heavy activity is immoral to some extent, and that theact of contributing to collective actions with foreseeable negative effects is morally questionable.These conclusions and their implications will be interpreted in a way that is compatible with thedefinition of responsibility that is used. Responsibility is considered to be a matter of degree forthe purposes of this essay. This will allow for a much wider range of relevant aspects to be takeninto consideration, when arguing for individual responsibility for climate harm

This study aims to investigate if there is any causal relationship between human rights and economic growth. The causality is tested in both directions, from human rights to economic growth and from economic growth to human rights, using a panel data Granger Causality test. The variable used to represent human rights is a human rights score and the variable used to represent economic growth is annual growth of real GDP per capita. Both of these variables are retrieved from Our World in Data. There is a total number of 81 countries included in this study with yearly observations from 1962 until 2017 on both variables. To achieve a greater depth the 81 countries were categorized into three different categories: low-income, middle-income and high-income countries. Previous studies and theories indicate that it is possible to expect a two-way causal relationship between economic growth and human rights. However, the results in this study indicate that there is no statistically significant causal relationship in any direction for any of the income categories.

Human mobility is a complex phenomenon emerging from the nexus between social, demographic, economic, political and environmental systems. In this thesis we develop novel mathematical models for the study of complex systems, to improve our understanding of mobility patterns and enhance our ability to predict local and global flows for real-world applications.The first and second chapters introduce the concept of human mobility from the point of view of complex systems science, showing the relation between human movements and their predominant drivers. In the second chapter in particular, we will illustrate the state of the art and a summary of our scientific contributions. The rest of the thesis is divided into three parts: structure, causes and effects.The third chapter is about the structure of a complex system: it represents our methodological contribution to Network Science, and in particular to the problem of network reconstruction and topological analysis. We propose a novel methodological framework for the definition of the topological descriptors of a complex network, when the underlying structure is uncertain. The most used topological descriptors are redefined – even at the level of a single node – as probability distributions, thus eluding the reconstruction phase. With this work we have provided a new approach to study the topological characteristics of complex networks from a probabilistic perspective. The forth chapter deals with the effects of human mobility: it represents our scientific contribution to the debate about the COVID-19 pandemic and its consequences. We present a complex-causal analysis to investigate the relationship between environmental conditions and human activity, considered as the components of a complex socio-environmental system. In particular, we derive the network of relations between different flavors of human mobility data and other social and environmental variables. Moreover, we studied the effects of the restrictions imposed on human mobility – and human activities in general – on the environmental system. Our results highlight a statistically significant qualitative improvement in the environmental variable of interest, but this improvement was not caused solely by the restrictions due to COVID-19 pandemic, such as the lockdown.The fifth and sixth chapters deal with the modelling of causes of human mobility: the former is a concise chapter that illustrate the phenomenon of human displacements caused by environmental disasters. Specifically, we analysed data from different sources to understand the factors involved in shaping mobility patterns after tropical cyclones. The latter presents the Feature-Enriched Radiation Model (FERM), our generalization of the Radiation Model which is a state-of-the-art mathematical model for human mobility. While the original Radiation Model considers only the population as a proxy for mobility drivers, the FERM can handle any type of exogenous information that is used to define the attractiveness of different geographical locations. The model exploits this information to divert the mobility flows towards the most attractive locations, balancing the role of the population distribution. The mobility patterns at different scales can be reshaped, following the exogenous drivers encoded in the features, without neglecting the global configuration of the system.

<p dir="ltr">Despite their astonishing capacity to fit data, neural networks have difficulties extrapolating beyond training data distribution. When the out-of-distribution prediction task is formalized as a counterfactual query on a causal model, the reason for their extrapolation failure is clear: neural networks learn spurious correlations in the training data rather than features that are causally related to the target label. This thesis proposes to perform a causal search over a known family of causal models to learn robust (maximally invariant) predictors for single- and multiple-environment extrapolation tasks.</p><p dir="ltr">First, I formalize the out-of-distribution task as a counterfactual query over a structural causal model. For single-environment extrapolation, I argue that symmetries of the input data are valuable for training neural networks that can extrapolate. I introduce Asymmetry learning, a new learning paradigm that is guided by the hypothesis that all (known) symmetries are mandatory even without evidence in training, unless the learner deems it inconsistent with the training data. Asymmetry learning performs a causal model search to find the simplest causal model defining a causal connection between the target labels and the symmetry transformations that affect the label. My experiments on a variety of out-of-distribution tasks on images and sequences show that proposed methods extrapolate much better than the standard neural networks.</p><p dir="ltr">Then, I consider multiple-environment out-of-distribution tasks in dynamical system forecasting that arise due to shifts in initial conditions or parameters of the dynamical system. I identify key OOD challenges in the existing deep learning and physics-informed machine learning (PIML) methods for these tasks. To mitigate these drawbacks, I combine meta-learning and causal structure discovery over a family of given structural causal models to learn the underlying dynamical system. In three simulated forecasting tasks, I show that the proposed approach is 2x to 28x more robust than the baselines.</p>

This thesis presents an innovative approach to enhance the stability of floating offshore wind turbine (FOWT) platform through mooring actuation. First, an OC3- Hywind spar-buoy floating platform is modeled utilizing the Control-oriented, Reconfigurable, and Acausal Floating Turbine Simulator (CRAFTS) with a specific focus on predicting hydrodynamic and mooring line loads while intentionally excluding consideration of aerodynamic forces. The accuracy of this model is validated against the industry standard OpenFAST simulator through various test cases. The central objective of this study revolves around achieving robust stabilization of the spar buoy platform, primarily focusing on X-Z symmetric planar motions, including surge, pitch, and heave degrees of freedom (DOFs). To accomplish this, two linearization techniques are employed: one transforms the inherently complex nonlinear model from CRAFTS into a linear Mass-Spring-Damper (MSD) system, particularly targeting surge and pitch motions, while the other method involves the conversion of the nonlinear model from CRAFTS into the Functional Mockup Interface (FMI) within MATLAB/Simulink for linearization. The analysis utilizing Bode plots derived from these lin- earized models yields crucial insights into the system's response to mooring actuation. Notably, it emphasizes the inherent challenge in pitch control, characterized by lower gain compared to surge at relevant frequencies, necessitating substantial mooring actuation or cable length modifications for effective pitch stabilization. Then, a Linear Quadratic Regulator (LQR) controller is designed to mitigate surge and pitch motions. Numerical simulations conducted across diverse scenarios reveal the inherent challenge in simultaneously mitigating surge and pitch motions using the original platform configuration. To address this challenge, a control co-design strategy is proposed, leading to the development of an optimized mooring line configuration that effectively stabilizes both motions with minimal adjustments. In summary, this thesis introduces a control-oriented modeling approach and an innovative control strategy to enhance the stability of the floating wind turbine platform through mooring actuation. The results emphasize the potential for broader application of this approach to various floating platforms for FOWTs and the extension of stabilization efforts to address all six DOFs in future research, where aerodynamic loads are also incorporated.