A DSEL for Studying and Explaining Causation

TL;DR

This paper introduces a Haskell-based domain-specific language that models, visualizes, and extends neuron diagrams for analyzing and explaining complex causation scenarios in philosophy and practical applications.

Contribution

It provides a novel, extensible DSEL for neuron diagrams that supports non-boolean values and systematic scenario generation, enhancing causation research and explanation.

Findings

Supports modeling of complex causal relationships

Enables visual explanations and scenario generation

Extends neuron diagrams to non-boolean values

Abstract

We present a domain-specific embedded language (DSEL) in Haskell that supports the philosophical study and practical explanation of causation. The language provides constructs for modeling situations comprised of events and functions for reliably determining the complex causal relationships that emerge between these events. It enables the creation of visual explanations of these causal relationships and a means to systematically generate alternative, related scenarios, along with corresponding outcomes and causes. The DSEL is based on neuron diagrams, a visual notation that is well established in practice and has been successfully employed for causation explanation and research. In addition to its immediate applicability by users of neuron diagrams, the DSEL is extensible, allowing causation experts to extend the notation to introduce special-purpose causation constructs. The DSEL also extends the notation of neuron diagrams to operate over non-boolean values, improving its expressiveness and offering new possibilities for causation research and its applications.