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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.
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 theory provides solutions to a number of problems in causal inference, including questions of confounding control, policy analysis, mediation, missing data and the integration of data from diverse studies.
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
A diagram, or graphical causal model, representing the causal hypothesis is seen in Figure 14.1. Figure 14.1: A simple graphical causal model expressing the hypothesis that age when learning to drive is the causal factor for the difficulty of learning to drive.
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:
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.
