A conditional approach to inference after model selection

Model selection can invalidate inference, such as significance tests, but statisticians have recently made progress developing methods to adjust for this bias. One approach uses conditional probability, adjusting inferences by conditioning on selecting the chosen model. This post motivates the conditional approach with a simple screening rule example and introduces the selectiveInference R package that can compute adjusted significance tests after popular model selection methods like forward stepwise and LASSO. [Read More]