Simplifying Causality: A Brief Review of Philosophical Views and Definitions with Examples from Economics, Education, Medicine, Policy, Physics and Engineering

This short paper compiles the big ideas behind some philosophical views, definitions, and examples of causality. This collection spans the realms of the four commonly adopted approaches to causality: Hume's regularity, counterfactual, manipulation, and mechanisms. This short review is motivated by presenting simplified views and definitions and then supplements them with examples from various fields, including economics, education, medicine, politics, physics, and engineering. It is the hope that this short review comes in handy for new and interested readers with little knowledge of causality and causal inference. Introduction Causality is the science of cause and effect [1]. As identifying causal mechanisms is often regarded as a fundamental purist in most sciences, causality becomes elemental in advancing our knowledge. While causality is more profound in some research areas, the concept of causality is often vague or forgotten in others [2]. With the advent of data science and the ready accessibility of big data, there is a rising potential to leverage such data in pursuit of unlocking previously unknown, hidden mechanisms or perhaps confirming ongoing hypotheses and empirical knowledge [3]. Traditionally, researchers would collect data pertaining to a phenomenon and then analyze this data to describe it, creating a model that could be used to predict such a phenomenon and/or causally infer (or explain/understand) interesting questions about such a phenomenon (see Table 1) [4]. When the primary goal is to describe the data on hand, the researcher simply aims to visualize the data to tell its story.