A Network-Based Deterministic Model for Causal Complexity

Despite the widespread use of techniques and tools for causal analysis, existing methodologies still fall short as they largely regard causal variables as independent elements, thereby failing to appreciate the significance of the interactions of causal variables. The prospect of inferring causal relationships from weaker structural assumptions compels for further research in this area. This study explores the effects of the interactions of variables in the context of causal analysis, and introduces new advancements to this area of research. In this study, we introduce a new approach for the causal complexity with the goal of making the solution set closer to deterministic by taking into consideration the underlying patterns embedded within a dataset; in particular, the interactions of causal variables. Our model follows the configurational approach, and as such, is able to account for the three major phenomena of conjunctural causation, equifinality, and causal asymmetry

Flexible Two-Level Boolean Minimizer BOOM-II and Its Applications

We propose a novel two-level Boolean minimizer coming in succession to our previously developed minimizer BOOM, so we have named it BOOM-II. It is a combination of two minimizers, namely BOOM and FC-Min. Each of these two methods has its own area where it is most efficiently applicable. We have combined these two methods together to be able to solve all kinds of problems efficiently, independently on their size or nature. The tool is very scalable in terms of required runtime and/or quality of the solution. It is applicable to functions with an extremely large number of both input and output variables. The minimization process is very flexible and can be driven by miscellaneous user-defined constraints, such as low-power design, design-for-testability and decomposition constraints. Some of the application areas are described in the paper. 1