Colloquium Series: Lucas Janson

Our upcoming event for the Statistics Colloquium Series is scheduled for Monday, April 27 from 12:00 – 1:00pm (ET) and will be an in-person presentation at Maxwell-Dworkin 134A/B. Lunch will be provided to guests following the talk. This week's speaker will be faculty member Tracy Ke from our Statistics department.

Chiseling: Powerful and Valid Subgroup Selection via Interactive Machine Learning

In regression and causal inference, controlled subgroup selection aims to identify, with inferential guarantees, a subgroup (defined as a subset of the covariate space) on which the average response or treatment effect is above a given threshold. E.g., in a clinical trial, it may be of interest to find a subgroup with a positive average treatment effect. However, existing methods either lack inferential guarantees, heavily restrict the search for the subgroup, or sacrifice efficiency by naive data splitting. We propose a novel framework called chiseling that allows the analyst to interactively refine and test a candidate subgroup by iteratively shrinking it. The sole restriction is that the shrinkage direction only depends on the points outside the current subgroup, but otherwise the analyst may leverage any prior information or machine learning algorithm. Despite this flexibility, chiseling controls the probability that the discovered subgroup is null (e.g., has a non-positive average treatment effect) under minimal assumptions: for example, in randomized experiments, this inferential validity guarantee holds under only bounded moment conditions. When applied to a variety of simulated datasets and a real survey experiment, chiseling identifies substantially better subgroups than existing methods with inferential guarantees. This is joint work with Nathan Cheng and Asher Spector and is available at https://arxiv.org/abs/2509.19490.

Lucas Janson is a Professor of Statistics and Affiliate in Computer Science at Harvard University, where he study's high-dimensional inference and statistical machine learning.