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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.
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:
How does one recognize causal expressions in the statistical literature? Those versed in the potential-outcome notation (Neyman, 1923; Rubin, 1974; Holland, 1986), can recognize such expressions through the subscripts that are attached to counterfactual events and variables, for example, Yx(u) or Zxy—some authors use parenthetical expressions ...
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).
become important to understand how valid causal studies can be designed and how suspicious studies can be identi ed. This paper aims to further these understandings by explaining the statistical principles and techniques that underlie valid studies of causal relationships.
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
