This post explores principal stratification and mediation analysis as tools for understanding causal effects, decomposing them into direct and indirect components. It covers scenarios like non-compliance, missing outcomes, and surrogate indices, highlighting the importance of assumptions such as no direct effects and no Defiers. Practical methods, including multiple imputation, regression, and matching, are discussed for estimating effects even when key quantities are unobserved. Real-world examples, like marketing lift studies and product funnels, illustrate the relevance of these techniques for addressing complex causal questions.

In this note, we introduce a method for interpreting and validating the results of uplift modeling. We propose two novel strategies for controlling the Familywise Error Rate in this setting.

Randomization provides the “reasoned basis for inference” in an experiment. Yet some approaches to analyzing experiments ignore the special structure of randomization. Simple, familiar approaches like regression models sometimes give wrong answers when applied to experiments. Approaches exploiting randomization deliver more reliable inferences than methods neglecting it. Randomization inference should be the first method we reach for when analyzing experiments.