Causality

Causality refers to the relationship between an event (the cause) and a second event (the effect), where the second event is a result of the first. In data science, causality is concerned with identifying whether a change in one variable causes a change in another variable. This is important for making predictions and understanding the impact of interventions. Causal inference is the process of drawing a conclusion about causality based on the evidence available. This involves controlling for confounding variables and establishing a temporal relationship between cause and effect. Causal models are used to represent the relationships between variables and to make predictions about the effect of interventions. These models can be based on statistical methods, machine learning algorithms, or domain knowledge.