Causal notation is notation used to express cause and effect.
In nature and human societies, many phenomena have causal relationships where one phenomenon A (a cause) impacts another phenomenon B (an effect). Establishing causal relationships is the aim of many scientific studies across fields ranging from biology and physics to social sciences and economics. It is also a subject of accident analysis, and can be considered a prerequisite for effective policy making.
Causality is an influence by which one event, process, state, or object (a cause) contributes to the production of another event, process, state, or object (an effect) where the cause is at least partly responsible for the effect, and the effect is at least partly dependent on the cause. The cause of something may also be described as the reason for the event or process.
In general, a process can have multiple causes, which are also said to be causal factors for it, and all lie in its past. An effect can in turn be a cause of, or causal factor for, many other effects, which all lie in its future. Thus, the distinction between cause and effect either follows from or else provides the distinction between past and future. While the former viewpoint is more prevalent in physics, some writers have held that causality is metaphysically prior to notions of time and space. Causality is an abstraction that indicates how the world progresses. As such, it is a basic concept, and one might expect it to be more apt as an explanation of other concepts of progression than something to be explained by yet more fundamental ideas. The concept is like those of agency and efficacy. For this reason, a leap of intuition may be needed to grasp it. Accordingly, causality is implicit in the structure of ordinary language, as well as explicit in the language of scientific causal notation.
In metaphysics and statistics, a causal model (also called a structural causal model) is a conceptual model that represents the causal mechanisms of a system. Causal models often employ formal causal notation, such as structural equation modeling or causal directed acyclic graphs (DAGs), to describe relationships among variables and to guide inference.
By clarifying which variables should be included, excluded, or controlled for, causal models can improve the design of empirical studies and the interpretation of results. They can also enable researchers to answer some causal questions using observational data, reducing the need for interventional studies such as randomized controlled trials.
Causal inference is the process of determining the independent, actual effect of a particular phenomenon that is a component of a larger system. The main difference between causal inference and inference of association is that causal inference analyzes the response of an effect variable when a cause of the effect variable is changed. The study of why things occur is called etiology, and can be described using the language of scientific causal notation. Causal inference is said to provide the evidence of causality theorized by causal reasoning.
Causal inference is widely studied across all sciences. Several innovations in the development and implementation of methodology designed to determine causality have proliferated in recent decades. Causal inference remains especially difficult where experimentation is difficult or impossible, which is common throughout most sciences.