Document worth reading: “An Introduction to Causal Inference”

This paper summarizes newest advances in causal inference and underscores the paradigmatic shifts which have to be undertaken in shifting from standard statistical analysis to causal analysis of multivariate info. Special emphasis is positioned on the assumptions that underlie all causal inferences, the languages utilized in formulating these assumptions, the conditional nature of all causal and counterfactual claims, and the methods which have been developed for the analysis of such claims. These advances are illustrated using a standard precept of causation based on the Structural Causal Model (SCM) described in Pearl (2000a), which subsumes and unifies completely different approaches to causation, and offers a coherent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the occasion of mathematical devices for inferring (from a combination of data and assumptions) options to three sorts of causal queries: these about (1) the outcomes of potential interventions, (2) prospects of counterfactuals, and (3) direct and indirect outcomes (additionally referred to as ‘mediation’). Finally, the paper defines the formal and conceptual relationships between the structural and potential-outcome frameworks and presents devices for a symbiotic analysis that makes use of the sturdy choices of every. The devices are demonstrated throughout the analyses of mediation, causes of outcomes, and prospects of causation. An Introduction to Causal Inference