Speaker |
David Watson |
|---|---|
Affiliation |
King's College London |
Date |
Friday, 15 November 2024 |
Time |
12:00-13:00 |
Location |
Room 05, Tottenham Court Road (188), London W1T 7PH |
Link |
https://ucl.zoom.us/j/7532311934 |
Event series |
Jump Trading/ELLIS CSML Seminar Series |
Abstract |
Instrumental variables (IVs) are a popular and powerful tool for estimating causal effects in the presence of unobserved confounding. However, classical approaches rely on strong assumptions such as the exclusion criterion, which states that instrumental effects must be entirely mediated by treatments. This assumption often fails in practice. When IV methods are improperly applied to data that do not meet the exclusion criterion, estimated causal effects may be badly biased. In this work, we propose a novel solution that provides partial identification in linear systems given a set of leaky instruments, which are allowed to violate the exclusion criterion to some limited degree. We derive a convex optimization objective that provides provably sharp bounds on the average treatment effect under some common forms of information leakage, and implement inference procedures to quantify the uncertainty of resulting estimates. We demonstrate our method in a set of experiments with simulated data, where it performs favorably against the state of the art. An accompanying R package, leakyIV, is available from CRAN. |
Biography |
David Watson is a Lecturer in Artificial Intelligence at King’s College London’s Department of Informatics. His primary research interests include machine learning, philosophy of science, and computational biology. Previously, he was a Postdoctoral Research Fellow in the Department of Statistical Science at University College London, where he developed methods for causal discovery and inference in collaboration with Prof. Ricardo Silva’s group. Before that, he earned his doctorate from the University of Oxford, studying algorithmic fairness and explainability under the supervision of Prof. Luciano Floridi. He is an Associate Editor at Minds & Machines, a Visiting Research Fellow at Meta’s Central Applied Science unit, and an occasional contributor to The Economist. |