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5 cze 2018 · We provide a conceptual map to navigate causal analysis problems. Focusing on the case of discrete random variables, we consider the case of causal effect estimation from observational data.
This chapter presents a brief overview of statistical estimation and infer-ence in randomized controlled trials (RCTs). When available, evidence drawn from RCTs is often considered gold standard statistical evidence; and thus methods for studying RCTs form the foundation of the statistical toolkit for causal inference.
Causal inference is driven by applications and is at the core of statistics (the science of using information discovered from collecting, organising, and studying numbers|Cambridge Dictionary).
The research questions that motivate most studies in statistics-based sciences are causal in nature. The aim of standard statistical analysis is to infer associations among variables. Causal analysis goes one step further; its aim is to infer aspects of the data generating process. In most cases, Association does not imply causation:
These advances are illustrated using a general theory of causation based on the Structural Causal Model (SCM) described in Pearl (2000a), which subsumes and unifies other approaches to causation, and provides a coher-ent mathematical foundation for the analysis of causes and counterfactuals.
Randomized controlled trials (RCTs) form the foundation of statistical causal inference. When available, evidence drawn from RCTs is often considered gold standard evidence; and even when RCTs cannot be run for ethical or practical reasons, the quality of observational studies is often assessed in terms of how
using causal language in statistics. Nevertheless, for many users of statistical methods, causal statements are exactly what they seek. The fundamental notion underlying our approach is that causality is tied to an action (or manipulation, treatment, or intervention), applied to a unit.
