[Statlist] Reminder: ETH/UZH Research Seminar on Statistics by Linbo Wang, University of Toronto -- ETH Zurich, 2 April 2025

[Maurer Letizia]

We are glad to announce and invite you to the following talk in the ETH/UZH Research Seminar on Statistics:

"The synthetic instrument: From sparse association to sparse causation"  
by Linbo Wang, University of Toronto

Date and Time: Wednesday, 2 April 2025 at 15.15 h
Place: ETH Zurich, HG G 19.1

Abstract: In many observational studies, researchers are often interested in studying the effects of multiple exposures on a single outcome. Standard approaches for high-dimensional data such as the lasso assume the associations between the exposures and the outcome are sparse. These methods, however, do not estimate the causal effects in the presence of unmeasured confounding. In this paper, we consider an alternative approach that assumes the causal effects in view are sparse. We show that with sparse causation, the causal effects are identifiable even with unmeasured confounding. At the core of our proposal is a novel device, called the synthetic instrument, that in contrast to standard instrumental variables, can be constructed using the observed exposures directly. We show that under linear structural equation models, the problem of causal effect estimation can be formulated as an ℓ0-penalization problem, and hence can be solved efficiently using off-the-shelf software. Simulations show that our approach outperforms state-of-art methods in both low-dimensional and high-dimensional settings. We further illustrate our method using a mouse obesity dataset.

Seminar website, https://math.ethz.ch/sfs/news-and-events/research-seminar.html


Organisers:  A. Bandeira, P. Bühlmann, Y. Chen, R. Furrer, L. Held, T. Hothorn, D. Kozbur,  J. Peters,  M. Wolf, J. Ziegel