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Jul7
EVENT DETAILS
Professor Zhimei Ren
University of Chicago
Title: Sensitivity Analysis of Individual Treatment Effects: A Robust Conformal Inference Approach
Abstract: We propose a model-free framework for sensitivity analysis of individual treatment effects (ITEs), building upon ideas from conformal inference. For any unit, our procedure reports the ¦£-value, a number which quantifies the minimum strength of confounding needed to explain away the evidence for ITE. Our approach rests on the reliable predictive inference of counterfactuals and ITEs in situations where the training data is confounded. Under the marginal sensitivity model of Tan (2006), we characterize the shift between the distribution of the observations and that of the counterfactuals. We first develop a general method for predictive inference of test samples from a shifted distribution; we then leverage this to construct covariate-dependent prediction sets for counterfactuals. No matter the value of the shift, these prediction sets (resp. approximately) achieve marginal coverage if the propensity score is known exactly (resp. estimated). We describe a distinct procedure also attaining coverage, however, conditional on the training data. In the latter case, we prove a sharpness result showing that for certain classes of prediction problems, the prediction intervals cannot possibly be tightened. We verify the validity and performance of the new methods via simulation studies and apply them to analyze real datasets.
This is joint work with Ying Jin and Emmanuel Cand¨¨s.
Bio: Zhimei Ren is currently a postdoctoral researcher in the Statistics department at the University of Chicago, working with Prof. Rina Foygel Barber. Before coming to the University of Chicago, she received her Ph.D. in Statistics from Stanford University, under the supervision of Prof. Emmanuel Cand¨¨s, and prior to this she received her B.S. in Statistics from Peking University. Her research interests lie broadly in the span of multiple hypothesis testing, causal inference, survival analysis, distribution-free inference and data-driven decision-making.
TIME Thursday, July 7, 2022 at 11:00 AM - 12:00 PM
LOCATION ITW 1.350, Ford Motor Company Engineering Design Center map it
CONTACT Agnes Kaminski a-kaminski@northwestern.edu EMAIL
CALENDAR Department of Industrial Engineering and Management Sciences
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Sep20
EVENT DETAILS
Fall classes begin 8 a.m.
TIME Tuesday, September 20, 2022
CONTACT Office of the Registrar nu-registrar@northwestern.edu EMAIL
CALENDAR University Academic Calendar