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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).
In example 1, the lurking variable has an effect on both the explanatory and the response variables, creating the illusion that there is a causal link between them. In example two, the lurking variable is confounded with the explanatory variable, making it hard to assess the isolated effect of the explanatory variable on the response variable.
Under unconfoundedness, the causal effects are identified from the observed data: First conditional on subpopulations with covariate balance (via e.g., randomization, or matching, stratification), calculate the difference between treatment and control groups.
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
This course provides an introduction to causal and statistical reasoning. After taking this course, students will be better prepared to make rational decisions about their own lives and about matters of social policy.
