2026 Fall

Date: 2026-10-30

Time: 15:30-16:30 (Montreal time)

Location: In person, Burnside 1104

https://mcgill.zoom.us/j/82510837324

Meeting ID: 825 1083 7324

Abstract:

Testing statistical software presents unique challenges. The space of possible inputs, options, and workflows can be enormous, making exhaustive testing impractical. At the same time, software developers often have limited time and resources for formal testing activities.

This talk explores how principles from Design of Experiments (DOE) can be applied to the software test case selection problem. By viewing test cases as experimental runs and software inputs as factors, DOE provides a structured framework for constructing efficient test suites that maximize information while minimizing effort. The discussion highlights connections between experimental design and software testing, demonstrating how statistical thinking can improve test coverage, identify defects more effectively, and increase confidence in software reliability. More broadly, the talk illustrates how core ideas from DOE can be successfully applied in nontraditional settings.

Date: 2026-10-23

Time: 15:30-16:30 (Montreal time)

Location: Online via Zoom

https://mcgill.zoom.us/j/83173904340

Meeting ID: 831 7390 4340

Abstract:

Targets of inference in nonparametric instrumental variables, proximal causal inference, and shadow-variable models are often continuous linear functionals of solutions to a pair of ill-posed inverse problems. Recent work has shown that, under minimal assumptions, ill-posedness does not preclude pointwise root-n inference. We show that pointwise guarantees need not translate into locally reliable inference. In a baseline model sufficient for a Neyman orthogonal estimating equation to exist, the target is Hellinger discontinuous, which rules out locally uniformly valid inference. A stronger model that uniformly bounds the minimum-norm solution to one of the inverse problems restores continuity. However, pathwise differentiability then holds only if that solution is unique at the true data-generating process. When it fails, Wald-type confidence intervals from standard procedures undercover asymptotically under some local alternatives. Pathwise differentiability is also restored, even without uniqueness, under a uniform bound on a beta-source norm. We relate these regimes to very weak, weak, and strong identification in linear IV models.

Date: 2026-09-25

Time: 15:30-16:30 (Montreal time)

Location: In person, Burnside 1104

https://mcgill.zoom.us/j/89492447923

Meeting ID: 894 9244 7923

Abstract:

While classical statistical methods are designed for testing hypotheses about pre-specified models, the reality of modern science is that analysts often explore their data before coming up with models and hypotheses of interest. We refer to the practice of using the same data to generate and then test a hypothesis as double dipping. Classical statistical hypothesis tests will fail to control the Type 1 error rate in settings that involve double dipping. Often, we avoid double dipping by splitting our observations into a training set and a test set. While this sample splitting approach is straightforward and easy to understand, it is generally inapplicable in unsupervised settings. Motivated by unsupervised problems that arise in the analysis of single-cell RNA sequencing data, we first propose data thinning, an alternative to sample splitting that splits each observation in a dataset into two independent pieces. We show that this method provides an elegant solution to our motivating problems under distributional assumptions. We then show how the data fission framework of Leiner et al. (2025) can be fruitfully applied to circumvent the distributional assumptions of data thinning. We conclude by discussing the promise and the challenges of data fission in the context of inference after clustering and inference after variable selection in logistic regression.