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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).
Goal: evaluate the effect of statins on health outcomes. Patients: cross-sectional population from the offspring cohort with a visit 6 (1995-1998) Treatment: statin use at visit 6 vs. no statin use. Outcomes: CV death, myocardial infarction (MI), stroke.
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
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.
Causal Learning. Patricia W. Cheng and Marc J. Buehner. Abstract. This chapter is an introduction to the psychology of causal inference using a computational perspective, with the focus on causal discovery. It explains the nature of the problem of causal discovery and illustrates the goal of the process with everyday and hypothetical examples.
