This dissertation explores the relationship between mental properties and physicalism to confront the apparent inconsistency between mental realism and the tenets of physicalism. As I see it, the major obstacle to fully integrating mental properties into physicalism is the feasibility of downward mental causation. Specifically, stringent physicalists find it contradictory to maintain that the mind can affect the body without contradicting the tenets of physicalism. This inconsistency claim is most notably addressed in the Causal Exclusion Argument. Though I am not personally committed to physicalism as an absolute worldview, I respect its prevalence and the reasons for its dominance. Rather than reject physicalism, I approach the puzzle with epistemological humility and attempt to work within the scope of physicalism. This exploration involves critically examining physicalism’s leading mental-physical relationships, focusing on emergence as a plausible means to reconcile mental and physical properties without undermining either. Ultimately, I propose a modified form of physicalism that maintains its metaphysical and epistemological theses but in a milder form that is more conducive to emergent mental phenomena and the aspects of reality that are nonlinear and indeterminate. Guided by the work of Jaegwon Kim and Gerald Vision, this dissertation moves beyond their ideas, challenging reductionist perspectives within physicalism. The key contribution is the introduction of Dynamically Stable Causal Holism (or DSC Holism in brief), which represents a significant departure from traditional reductionist approaches, promoting a more holistic understanding of physicalism. Through nonlinear emergence and DSC Holism, I confront the Causal Exclusion Argument. A secondary original contribution is my approach to these puzzles. I integrate and synthesize concepts from the philosophy of science and special sciences to offer a fresh perspective on physically compatible mental realism and downward causation. / Philosophy

Russellian panpsychism is often regarded as a theory of mind that bears promise of integrating conscious experience into the physical causal order. In a recent article by Howell, this is questioned. I will argue that failure to address Howell´s challenge properly has deeper consequences than it might initially appear; epiphenomenal micro-qualia means that we have lost a unique opportunity to gain insight into necessities in nature. In order to make use of this opportunity, however, some initial assumptions commonly made must be dropped: most crucially, the assumption of mind-body distinctness. In what follows, I try to provide a sketch of how a slightly different version of Russellian panpsychism can be formulated that builds around identity instead of mind-body distinctness. This version of panpsychism can meet Howell's challenge, but what is more, it can be met in a way that fully makes use of the special place occupied by panpsychism regarding the mysterious nature of the “necessary connection” between cause and effect.

Automatic Root Cause Analysis (RCA) systems aim to streamline the process of identifying the underlying cause of software failures in complex cloud-native environments. These systems employ graph-like structures to represent causal relationships between different components of a software application. These relationships are typically learned through performance and resource utilization metrics of the microservices in the system. To accomplish this objective, numerous RCA systems utilize statistical algorithms, specifically those falling under the category of causal discovery. These algorithms have demonstrated their utility not only in RCA systems but also in a wide range of other domains and applications. Nonetheless, there exists a research gap in the exploration of the feasibility and efficacy of multivariate time series causal discovery algorithms for deriving causal graphs within a microservice framework. By harnessing metric time series data from Prometheus and applying these algorithms, we aim to shed light on their performance in a cloudnative environment. Furthermore, we have introduced an adaptation in the form of an ensemble causal discovery algorithm. Our experimentation with this ensemble approach, conducted on datasets with known causal relationships, unequivocally demonstrates its potential in enhancing the precision of detected causal connections. Notably, our ultimate objective was to ascertain reliable causal relationships within Ericsson’s cloud-native system ’X,’ where the ground truth is unavailable. The ensemble causal discovery approach triumphs over the limitations of employing individual causal discovery algorithms, significantly augmenting confidence in the unveiled causal relationships. As a practical illustration of the utility of the ensemble causal discovery techniques, we have delved into the domain of anomaly detection. By leveraging causal graphs within our study, we have successfully applied this technique to anomaly detection within the Ericsson system. / System för automatisk rotorsaksanalys (RCA) syftar till att effektivisera process för att identifiera den underliggande orsaken till programvarufel i komplexa molnbaserade miljöer. Dessa system använder grafliknande strukturer att representera orsakssamband mellan olika komponenter i en mjukvaruapplikation. Dessa relationer lär man sig vanligtvis genom prestanda och resursutnyttjande mätvärden för mikrotjänsterna i systemet. För att uppnå detta mål använder många RCAsystem statistiska algoritmer, särskilt de som faller under kategorin orsaksupptäckt. Dessa algoritmer har visat att de inte är användbara endast i RCA-system men även inom en lång rad andra domäner och applikationer. Icke desto mindre finns det en forskningslucka i utforskningen av genomförbarhet och effektivitet av orsaksupptäckt av multivariat tidsserie algoritmer för att härleda kausala grafer inom ett mikrotjänstramverk. Genom att utnyttja metriska tidsseriedata från Prometheus och tillämpa Dessa algoritmer strävar vi efter att belysa deras prestanda i ett moln- inhemsk miljö. Dessutom har vi infört en anpassning i formen av en ensemble kausal upptäcktsalgoritm. Vårt experiment med denna ensemblemetod, utförd på datauppsättningar med kända orsakssamband relationer, visar otvetydigt sin potential för att förbättra precisionen hos upptäckta orsakssamband. Särskilt vår ultimata Målet var att fastställa tillförlitliga orsakssamband inom Ericssons molnbaserade systemet ’X’, där grundsanningen inte är tillgänglig. De ensemble kausal discovery approach segrar över begränsningarna av att använda individuella kausala upptäcktsalgoritmer, avsevärt öka förtroendet för de avslöjade orsakssambanden. Som en praktisk illustration av nyttan av ensemblens kausal upptäcktstekniker har vi fördjupat oss i anomalidomänen upptäckt. Genom att utnyttja kausala grafer inom vår studie har vi framgångsrikt tillämpat denna teknik för att detektera anomali inom Ericsson system

A common task in data science at internet companies is to develop metrics that capture aspects of the user experience. In this thesis, we are interested in systems of measurement variables without direct causal relations such that covariance is explained by unobserved latent common causes. A framework for modeling the data generating process is given by Neuro-Causal Factor Analysis (NCFA). The graphical model consists of a directed graph with edges pointing from the latent common causes to the measurement variables; its functional relations are approximated with a constrained Variational Auto-Encoder (VAE). We refine the estimation of the graphical model by developing an MCMC algorithm over Bayesian networks from which we read marginal independence relations between the measurement variables. Unlike standard independence testing, the method is guaranteed to yield an identifiable graphical model. Our algorithm is competitive with the benchmark, and it admits additional flexibility via hyperparameters that are natural to the approach. Tuning these parameters yields superior performance over the benchmark. We train the improved NCFA model on Spotify user behavior data. It is competitive with the standard VAE on data reconstruction with the benefit of causal interpretability and model identifiability. We use the learned latent space representation to characterize clusters of Spotify users. Additionally, we train an NCFA model on data from a randomized control trial and observe treatment effects in the latent space. / En vanlig uppgift för en data scientist på ett internetbolag är att utveckla metriker som reflekterar olika aspekter av användarupplevelsen. I denna uppsats är vi intresserade av system av mätvariabler utan direkta kausala relationer, så till vida att kovarians förklaras av latenta gemensamma orsaker. Ett ramverk för att modellera den datagenererande processen ges av Neuro-Causal Factor Analysis (NCFA). Den grafiska modellen består av en riktad graf med kanter som pekar från de latenta orsaksvariablerna till mätvariablerna; funktionssambanden uppskattas med en begränsad Variational Auto-Encoder (VAE). Vi förbättrar uppskattningen av den grafiska modellen genom att utveckla en MCMC algoritm över Bayesianska nätverk från vilka vi läser de obetingade beroendesambanden mellan mätvariablerna. Till skillnad från traditionella oberoendetest så garanterar denna metod en identifierbar grafisk modell. Vår algoritm är konkurrenskraftig jämfört med referensmetoderna, och den tillåter ytterligare flexibilitet via hyperparametrar som är naturliga för metoden. Optimal justering av dessa hyperparametrar resulterar i att vår metod överträffar referensmetoderna. Vi tränar den förbättrade NCFA modellen på data om användarbeteende på Spotify. Modellen är konkurrenskraftig jämfört med en standard VAE vad gäller rekonstruktion av data, och den tillåter dessutom kausal tolkning och identifierbarhet. Vi analyserar representationen av Spotify-användarna i termer av de latenta orsaksvariablerna. Specifikt så karakteriserar vi grupper av liknande användare samt observerar utfall av en randomiserad kontrollerad studie.

Environmental pressures from consumer products and mechanisms of predetermination were examined in this thesis using causal loop diagram (CLD) and life cycle assessment (LCA) footprinting to respectively illustrate and provide some indicators about these mechanisms. Theoretical arguments and their practical implications were subjected to qualitative and quantitative analysis, using secondary and primary data. A study integrating theories from various research fields indicated that combining product-service system offerings and environmental policy instruments can be a salient aspect of the system change required for decoupling economic growth from consumption and environmental impacts. In a related study, modes of system behaviour identified were related to some pervasive sustainability challenges to the design of electronic products. This showed that because of consumption and investment dynamics, directing consumers to buy more expensive products in order to restrict their availability of money and avoid increased consumption will not necessarily decrease the total negative burden of consumption. In a study examining product systems, those of washing machines and passenger cars were modelled to identify variables causing environmental impacts through feedback loops, but left outside the scope of LCA studies. These variables can be considered in LCAs through scenario and sensitivity analysis. The carbon, water and energy footprint of leather processing technologies was measured in a study on 12 tanneries in seven countries, for which collection of primary data (even with narrow systems boundaries) proved to be very challenging. Moreover, there were wide variations in the primary data from different tanneries, demonstrating that secondary data should be used with caution in LCA of leather products. A study examining pre-consumer waste developed a footprint metric capable of improving knowledge and awareness among producers and consumers about the total waste generated in the course of producing products. The metric was tested on 10 generic consumer goods and showed that quantities, types and sources of waste generation can differ quite radically between product groups. This revealed a need for standardised ways to convey the environmental and scale of significance of waste types and for an international standard procedure for quantification and communication of product waste footprint. Finally, a planning framework was developed to facilitate inclusion of unintended environmental consequences when devising improvement actions. The results as a whole illustrate the quality and relevance of CLD; the problems with using secondary data in LCA studies; difficulties in acquiring primary data; a need for improved waste declaration in LCA and a standardised procedure for calculation and communication of the waste footprint of products; and systems change opportunities for product engineers, designers and policy makers. / <p><strong>Jury committee</strong></p><p></p><p><strong>Henrikke Baumann, </strong>Associate Professor<strong></strong></p><p>Chalmers University of Technology</p><p>Department of Energy and Environment</p><p>Division of Environmental System Analysis</p><p></p><p><strong>Joakim Krook, </strong>Associate Professor</p><p>Linköpings Universitet</p><p>Department of Management and Engineering (IEI) / Environmental Technology and Management (MILJÖ)</p><p></p><p><strong>Karl Johan Bonnedal, </strong>Associate Professor</p><p>Umeå University</p><p>Umeå School of Business and Economics (USBE)</p><p></p><p><strong>Sofia Ritzén</strong>, Professor</p><p>KTH Royal Institute of Technology</p><p>School of Industrial Engineering and Management</p><p>Department of Machine Design</p><p>Integrated Product Development</p><p>QC 20160405</p><p></p>

Causal reasoning is an important part of scientific thinking, and even young children can use causes to explain what they observe and to make predictions. Weight is an interesting type of cause because it is a nonobvious property, and thus is not readily observable. The first research question of my dissertation examines when children use this property as a cause. In Study 1, 2- to 5-year-old children completed three different tasks in which they had to use weight to produce effects; an object displacement task, a balance-scale task, and a tower building task. The children’s use of weight improved with age, with 4- and 5-year-olds showing above-chance performance on all tasks. The younger children’s performance was more variable across tasks, suggesting that the complexity of the problem may influence their use of weight. The second research question is whether children’s use of weight as a cause can be improved. To examine this question, I varied the pedagogical cues that children received on the balance scale task from Study 1. The results of Study 2, indicate that highlighting the different effects of the heavy and light objects improves 3- to 4-year-olds’ performance. However, the results of Study 3 indicate that 2-year-olds did not benefit from even multiple pedagogical cues (contrasting the different effects and providing a verbal description to highlight the weight difference). To sum up, children at age 4 and above showed a general ability to use weight in across causal reasoning tasks. Whether children’s understanding of weight could be improved depended on their age and the cues given.

The de Broglie-Bohm causal interpretation of quantum mechanics is discussed, and applied to the hydrogen atom in several contexts. Prominent critiques of the causal program are noted and responses are given; it is argued that the de Broglie-Bohm theory is of notable interest to physics. Using the causal theory, electron trajectories are found for the conventional Schr??dinger, Pauli and Dirac hydrogen eigenstates. In the Schr??dinger case, an additional term is used to account for the spin; this term was not present in the original formulation of the theory but is necessary for the theory to be embedded in a relativistic formulation. In the Schr??dinger, Pauli and Dirac cases, the eigenstate trajectories are shown to be circular, with electron motion revolving around the <i>z</i>-axis. Electron trajectories are also found for the 1<i>s</i>-2<i>p</i>0 transition problem under the Schr??dinger equation; it is shown that the transition can be characterized by a comparison of the trajectory to the relevant eigenstate trajectories. The structures of the computed trajectories are relevant to the question of the possible evolution of a quantum distribution towards the standard quantum distribution (quantum equilibrium); this process is known as quantum relaxation. The transition problem is generalized to include all possible transitions in hydrogen stimulated by semi-classical radiation, and all of the trajectories found are examined in light of their implications for the evolution of the distribution to the standard distribution. Several promising avenues for future research are discussed.

Instrumental variables have been used for a long time in the econometrics literature for the identification of the causal effect of one random variable, B, on another, C, in the presence of unobserved confounders. In the classical continuous linear model, the causal effect can be point identified by studying the regression of C on A and B on A, where A is the instrument. An instrument is an instance of a supplementary variable which is not of interest in itself but aids identification of causal effects. The method of instrumental variables is extended here to generalised linear models, for which only bounds on the causal effect can be computed. For the discrete instrumental variable model, bounds have been derived in the literature for the causal effect of B on C in terms of the joint distribution of (A,B,C). Using an approach based on convex polytopes, bounds are computed here in terms of the pairwise (A,B) and (A,C) distributions, in direct analogy to the classic use but without the linearity assumption. The bounding technique is also adapted to instrumental models with stronger and weaker assumptions. The computation produces constraints which can be used to invalidate the model. In the literature, constraints of this type are usually tested by checking whether the relative frequencies satisfy them. This is unsatisfactory from a statistical point of view as it ignores the sampling uncertainty of the data. Given the constraints for a model, a proper likelihood analysis is conducted to develop a significance test for the validity of the instrumental model and a bootstrap algorithm for computing confidence intervals for the causal effect. Applications are presented to illustrate the methods and the advantage of a rigorous statistical approach. The use of covariates and intermediate variables for improving the efficiency of causal estimators is also discussed.

Injuries have emerged as one of the biggest public health issues of the 21th century. Yet, the causal effects of injury control strategies are often questioned due to a lack of randomized experiments. In this thesis, a set of quasi-experimental methods are applied and discussed in the light of causal inference theory and the type of data commonly available in injury surveillance systems. I begin by defining the interrupted time series design as a special case of the regression-discontinuity design, and the method is applied to two empirical cases. The first is a ban on the sale and production of non-reduced ignition propensity (RIP) cigarettes, and the second is a tightening of the licensing rules for mopeds. A two-way fixed effects model is then applied to a case with time-varying starting dates, attempting to identify the causal effects of municipality-provided home help services for the elderly. Lastly, the effect of the Swedish bicycle helmet law is evaluated using the comparative interrupted time series and synthetic control methods. The results from the empirical studies suggest that the stricter licensing rules and the bicycle helmet law were effective in reducing injury rates, while the home help services and RIP cigarette interventions have had limited or no impact on safety as measured by fatalities and hospital admissions. I conclude that identification of the impact of injury control interventions is possible using low cost means. However, the ability to infer causality varies greatly by empirical case and method, which highlights the important role of causal inference theory in applied intervention research. While existing methods can be used with data from injury surveillance systems, additional improvements and development of new estimators specifically tailored for injury data will likely further enhance the ability to draw causal conclusions in natural settings. Implications for future research and recommendations for practice are also discussed. / Injuries have emerged as one of the biggest public health issues of the 21th century. Yet, the causal effects of injury control strategies are rarely known due to a lack of randomized experiments. In this thesis, a set of quasi-experimental methods are discussed in the light of causal inference theory and the type of data commonly available in injury surveillance systems. I begin by defining the identifying assumptions of the interrupted time series design as a special case of the regression-discontinuity design, and the method is applied to two empirical cases. The first is a ban on the sale and production of non-fire safe cigarettes and the second is a tightening of the licensing rules for mopeds. A fixed effects panel regression analysis is then applied to a case with time-varying starting dates, attempting to identify the causal effects of municipality-provided home help services for the elderly. Lastly, the causal effect of the Swedish bicycle helmet law is evaluated using a comparative interrupted time series design and a synthetic control design. I conclude that credible identification of the impact of injury control interventions is possible using simple and cost-effective means. Implications for future research and recommendations for practice are discussed.

In recent years, there has been a growing need for causal analysis in many modern stream applications such as web page click monitoring, patient health care monitoring, stock market prediction, electric grid monitoring, and network intrusion detection systems. The detection and prediction of causal relationships help in monitoring, planning, decision making, and prevention of unwanted consequences. An event stream is a continuous unbounded sequence of event instances. The availability of a large amount of continuous data along with high data throughput poses new challenges related to causal modeling over event streams, such as (1) the need for incremental causal inference for the unbounded data, (2) the need for fast causal inference for the high throughput data, and (3) the need for real-time prediction of effects from the events seen so far in the continuous event streams. This dissertation research addresses these three problems by focusing on utilizing temporal precedence information which is readily available in event streams: (1) an incremental causal model to update the causal network incrementally with the arrival of a new batch of events instead of storing the complete set of events seen so far and building the causal network from scratch with those stored events, (2) a fast causal model to speed up the causal network inference time, and (3) a real-time top-k predictive query processing mechanism to find the most probable k effects with the highest scores by proposing a run-time causal inference mechanism which addresses cyclic causal relationships. In this dissertation, the motivation, related work, proposed approaches, and the results are presented in each of the three problems.