Wrapping up the series on causal inference, this final post covers the essential topic of design sensitivity, which allows a statistician to derive actual insights from an observational study by making some necessary adjustments to the standard statistical inference used in randomized experiments. »
Continuing in the series on causal inference, this post discusses analyzing the results of a pair matched trial design with Wilcoxon's signed rank test and how to compute approximate p-values via normal approximation. »
Continuing in the causal inference series, this post discusses pair matched trial design via propensity scores and the "naive" model of observational studies. »
This is my master's thesis broken into smaller, more digestible pieces. Causal inference is a fascinating (and relatively emergent) branch of statistics that seeks to establish causal relationships between variables. It turns out that establishing causality is intensely more demanding than establishing associations via traditional statistical inference methods. This post covers the groundwork to get started with causal inference, including essential background about randomized experiments and observational studies. »