Casual reasoning in data science and Neutrosophic statistics

This thesis explores the intersection of causal reasoning, data science, and Neutrosophic statistics, proposing novel approaches to address uncertainty, indeterminacy, and inconsistency in causal analysis. The research aims to develop Neutrosophic causal models that offer a more nuanced representation of complex causal systems compared to classical approaches. By formulating new Neutrosophic statistical techniques, the study seeks to enable more robust quantification of causal effects from observational data, accounting for various sources of uncertainty. A key objective is the design of Neutrosophic causal reasoning algorithms capable of uncovering causal structures from uncertain and noisy data, with the goal of demonstrating improved performance in identifying causal relationships compared to traditional methods. To illustrate the practical utility of these techniques, the research applies the developed Neutrosophic approaches to a comprehensive case study on Arctic Sea Ice decline, showcasing their ability to handle real-world uncertainties and provide more reliable insights into complex environmental phenomena. Furthermore, this thesis aims to provide guidelines for applying Neutrosophic statistics in causal analysis across various domains, facilitating broader adoption of these techniques in data science practice. By bridging the gap between theoretical advancements and practical applications, this research contributes to the evolving field of causal reasoning in data science, offering new tools and methodologies for researchers and practitioners dealing with uncertainty in causal analysis.