causeeffect

Cause and effect, often called causal relationships, describe how one event or condition (the cause) brings about another event or state (the effect). Causes can be single factors or combinations of factors, and effects can be immediate or delayed. The concept is central to science, philosophy, policy, and everyday reasoning, providing explanations and guiding predictions and interventions.

Types of causation include direct versus indirect causes, necessary versus sufficient causes, and causal chains or networks where multiple steps link cause to effect. A single outcome may have several contributing causes, and feedback loops can make effects act as new causes for later events.

Distinguishing causation from correlation is a fundamental challenge. Correlation means two variables vary together, but it does not prove that one causes the other. Establishing causality typically relies on experiments with randomization, counterfactual reasoning, or robust observational methods and controls. Causal diagrams (directed acyclic graphs) and frameworks such as the Rubin causal model or Hill criteria are used to formalize assumptions and assess plausibility.

Common methods include controlled experiments, natural or quasi-experiments, and statistical approaches such as regression with controls, instrumental variables, and propensity score matching. These methods aim to isolate the effect of a factor while accounting for confounding variables.

Applications span sciences, engineering, and policy, including risk assessment, diagnosis, and forecasting. Limitations arise from incomplete information, unmeasured confounding, nonlinearity, and evolving relationships over time.