Recent Developments in Post-Selection Inference

It is common in modern applications to use data-dependent model selection tools to select a promising model before drawing inference over the parameters of the selected model. However, this simple series of steps conceals a significant fault that is often left unattended: the act of selection biases the distributions of test statistics and makes standard inference procedures unsound. This is referred to as the problem of post-selection inference. We review recent work showing how to correct for this in certain common settings.