Randomization Inference
Modes of Inference in Randomized Experiments
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.
Attributable Effects
In a previous post, we discussed why randomization provides a reasoned basis for inference in an experiment. Randomization not only quantifies the plausibility of a causal effect but also allows us to infer something about the size of that effect.
The Reasoned Basis for Inference in Experiments
In his 1935 book, “Design of Experiments”, Ronald Fisher described randomization as the “reasoned basis for inference” in an experiment. Why do we need a “basis” at all, let alone a reasoned one?
Fisher's Exact Test
Simulation-based inference sits on a rigorous foundation.
