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.

Speaker

Ellen Graham received her PhD from the University of Washington Department of Biostatistics, where she was advised by Dr. Andrea Rotnitzky and worked on semiparametric theory for data fusion and related problems in causal inference. She is now a Postdoctoral Researcher at the Center for Causal Inference at the University of Pennsylvania, working with Eric Tchetgen Tchetgen and Ashkan Ertefaie on developing novel methods for causal analysis.