Zoekresultaten

Grounding Causality in Bayesian Networks Using Qualitative Reasoning

The complexity of analysing dynamical systems often lies in the difficulty to monitor each of their dynamic properties. In this article, we use qualitative models to present an exhaustive way of representing every possible state of a given system, and combine it with Bayesian networks to integrate quantitative information and reasoning under uncertainty. The result is a combined model able to give explanations relying on expert knowledge to predict the behaviour of a system. We illustrate our approach with a deterministic model to show how the combination is done, then extend this model to integrate uncertainty and demonstrate its benefits

The role of plausible rivalexplanations

The strengths of design-based research (DBR from now on) is that it teststheories in the real word of practice and in doing so generates newknowledge and artifacts useful to both the scientist and the practitioner.However, DBR’s strength might also be considered its weakness; becausethe research takes place in real-life settings, controlling for theinnumerable confounding variables is impossible. This means that theknowledge claims made on the basis of the research need to beunderstood not as a causal explanation, but rather as a plausibleinterpretation. This chapter looks at the concept of plausible rivalexplanations as a way to help design-based researchers to understandhow their knowledge claims can be better warranted by incorporating rivalexplanations into their research.

Studying the effects of intervention programmes on household energy saving behaviours using graphical causal models

Randomised controlled trials are strongly advocated to evaluate the effects of intervention programmes on household energy saving behaviours. While randomised controlled trials are the ideal, in many cases, they are not feasible. Notably, many intervention studies rely on voluntary participation of households in the intervention programme, in which case random selection and random assignment are seriously challenged. Moreover, studies employing randomised controlled trials typically do not study the underlying processes causing behaviour change. Yet, the latter is highly important to improve theory and practice. We propose a systematic approach to causal inference based on graphical causal models to study effects of intervention programmes on household energy saving behaviours when randomised controlled trials are not feasible. Using a simple example, we explain why such an approach not only provides a formal tool to accurately establish effects of intervention programmes, but also enables a better understanding of the processes underlying behaviour change.

Multiperiod research designs for causal inference between the built environment and travel behaviour

The role text structure inference skill plays for eighth graders’ expository text comprehension

The present study investigated whether text structure inference skill (i.e., the ability to infer overall text structure) has unique predictive value for expository text comprehension on top of the variance accounted for by sentence reading fluency, linguistic knowledge and metacognitive knowledge. Furthermore, it was examined whether the unique predictive value of text structure inference skill differs between monolingual and bilingual Dutch students or students who vary in reading proficiency, reading fluency or linguistic knowledge levels. One hundred fifty-one eighth graders took tests that tapped into their expository text comprehension, sentence reading fluency, linguistic knowledge, metacognitive knowledge, and text structure inference skill. Multilevel regression analyses revealed that text structure inference skill has no unique predictive value for eighth graders’ expository text comprehension controlling for reading fluency, linguistic knowledge and metacognitive knowledge. However, text structure inference skill has unique predictive value for expository text comprehension in models that do not include both knowledge of connectives and metacognitive knowledge as control variables, stressing the importance of these two cognitions for text structure inference skill. Moreover, the predictive value of text structure inference skill does not depend on readers’ language backgrounds or on their reading proficiency, reading fluency or vocabulary knowledge levels. We conclude our paper with the limitations of our study as well as the research and practical implications.

Transfering Targeted Maximum Likelihood Estimation for Causal Inference into Sports Science

Although causal inference has shown great value in estimating effect sizes in, for instance, physics, medical studies, and economics, it is rarely used in sports science. Targeted Maximum Likelihood Estimation (TMLE) is a modern method for performing causal inference. TMLE is forgiving in the misspecification of the causal model and improves the estimation of effect sizes using machine-learning methods. We demonstrate the advantage of TMLE in sports science by comparing the calculated effect size with a Generalized Linear Model (GLM). In this study, we introduce TMLE and provide a roadmap for making causal inference and apply the roadmap along with the methods mentioned above in a simulation study and case study investigating the influence of substitutions on the physical performance of the entire soccer team (i.e., the effect size of substitutions on the total physical performance). We construct a causal model, a misspecified causal model, a simulation dataset, and an observed tracking dataset of individual players from 302 elite soccer matches. The simulation dataset results show that TMLE outperforms GLM in estimating the effect size of the substitutions on the total physical performance. Furthermore, TMLE is most robust against model misspecification in both the simulation and the tracking dataset. However, independent of the method used in the tracking dataset, it was found that substitutes increase the physical performance of the entire soccer team.