Quiz

Causality refers to the influence of one event, process, state, or object on another, where the former is partly responsible for the latter. It is a basic concept that is implicit in the structure of language and explicit in scientific notation. Aristotle categorized four types of causes including material, formal, efficient, and final causes. David Hume argued that pure reason cannot prove the reality of efficient causality and that knowledge of it derives solely from experience. The topic of causality remains a staple in contemporary philosophy. Causality is a concern of metaphysics, and one viewpoint is that cause and effect are of one and the same kind of entity, with causality being an asymmetric relation between them. Another viewpoint is that causes and effects are 'states of affairs', with the exact natures of those entities being less restrictively defined than in process philosophy. Since causality is a subtle metaphysical notion, considerable intellectual effort, along with exhibition of evidence, is needed to establish knowledge of it in particular empirical circumstances. Causality has the properties of antecedence and contiguity, which are ingredients for space-time geometry. The notion of causality is metaphysically prior to the notions of time and space. Causes may sometimes be distinguished into two types: necessary and sufficient. A third type of causation, which requires neither necessity nor sufficiency in and of itself, but which contributes to the effect, is called a "contributory cause". Counterfactual theories define causation in terms of a counterfactual relation, and probabilistic causation refers to the notion that if A causes B, then A must always be followed by B, but the relationship is probabilistic rather than deterministic.Understanding Causality: Theories and Methods

Causal calculus is a theoretical framework that allows one to infer interventional probabilities from conditional probabilities in causal Bayesian networks with unmeasured variables.
The theory of causal calculus relies on the distinction between conditional probabilities and interventional probabilities.
The backdoor criterion provides a mathematical definition of confounding and helps researchers identify accessible sets of variables worthy of measurement.
Parts of the causal structure can be learned from statistical data, under certain assumptions.
The recovery algorithm rests on Sewall Wright's distinction between the three possible types of causal substructures allowed in a directed acyclic graph (DAG).
Path analysis and structural equation modeling serve better to estimate a known causal effect or to test a causal model than to generate causal hypotheses.
Causal direction can often be inferred if information about time is available.
Simon and Rescher claim that a causal relation is not a relation between values of variables, but a function of one variable on to another.
Manipulation theories equate causality with manipulability.
Process theories distinguish between causal processes and non-causal processes, and claim that causal processes can be identified by their ability to transmit an alteration over space and time.
The important concept for understanding causality is identifying causal processes.
Within the conceptual frame of the scientific method, an investigator sets up several distinct and contrasting temporally transient material processes that have the structure of experiments.Understanding Causality in Physics, Engineering, Psychology, Statistics and Economics
In physics, causality is not inherently implied in equations of motion, but postulated as an additional constraint that needs to be satisfied.
Causal notions are important in general relativity to the extent that the existence of an arrow of time demands that the universe's semi-Riemannian manifold be orientable.
In engineering, a causal system is a system with output and internal states that depends only on the current and previous input values.
In biology, medicine, and epidemiology, aspects of an association such as strength, consistency, specificity, and temporality are considered in attempting to distinguish causal from noncausal associations in the epidemiological situation.
Psychologists investigate how people and non-human animals detect or infer causation from sensory information, prior experience, and innate knowledge.
Attribution theory is the theory concerning how people explain individual occurrences of causation.
Within psychology, Patricia Cheng attempted to reconcile the Humean and Kantian views on causality.
Our view of causation depends on what we consider to be the relevant events.
Researchers are using neuroscience techniques to investigate the neural and psychological underpinnings of causal launching events in which one object causes another object to move.
Statistics and economics usually employ pre-existing data or experimental data to infer causality by regression methods.
The body of statistical techniques involves substantial use of regression analysis.
Here the notion of causality is one of contributory causality as discussed above.Understanding Causality in Philosophy, Economics, and Management
Causality is the relationship between an event (the cause) and a second event (the effect), where the second event is understood as a consequence of the first.
In economics, causality is tested by establishing that there is no reverse causation, by introducing other variables that are known to be unaffected by the dependent variable, or by including only variables that precede in time the dependent variable.
Regression analysis controls for other relevant variables by including them as explanatory variables, which helps to avoid false inferences of causality due to the presence of a third, underlying variable that influences both the potentially causative variable and the potentially caused variable.
Apart from constructing statistical models of observational and experimental data, economists use axiomatic (mathematical) models to infer and represent causal mechanisms.
In management, Kaoru Ishikawa developed a cause and effect diagram, known as an Ishikawa diagram or fishbone diagram, which categorizes causes into six main categories, and has spread beyond quality control to other areas of management and design and engineering.
Ishikawa diagrams have been criticized for failing to make the distinction between necessary conditions and sufficient conditions.
In history, events are sometimes considered as agents that can bring about other historical events, and causes are reified as ontological entities.
Philosophers of history have claimed that explanations in history and elsewhere describe not simply an event but a change.
Law and jurisprudence require that causality must be demonstrated to hold a defendant liable for a crime or a tort, and is also an essential legal element that must be proven to qualify for remedy measures under international trade law.
In Hindu philosophy, karma is the belief that a person's actions cause certain effects in the current life and/or in future life, positively or negatively.
In Buddhist philosophy, karma is the causality principle focusing on causes, actions, and effects, where it is the mind's phenomena that guide the actions that the actor performs.
Aristotle identified four kinds of answer or explanatory mode to various "Why?" questions, of which only one, the 'efficient cause,' is a cause as defined in the leading paragraph.