[Colloquium] Data Science/Stats Candidate Talk 2/3: Lihua Lei (Stanford)

[Rob Mitchum]

*Data Science Institute/Statistics Candidate Seminar*

*Lihua Lei*
*Postdoctoral Researcher*
*Stanford University*

*Thursday, February 3rd*
*3:30 p.m. - 4:30 p.m.*
*Remote Only: Live Stream <http://live.cs.uchicago.edu/lihualei/> or Zoom
<https://uchicago.zoom.us/j/94714255217?pwd=Rm1PQ2VrRVFXTU9MZHUwV0ZXejQ4UT09>
**(details
below)*

*Abstract*: Valid uncertainty quantification is crucial for high-stakes
decision-making. Conformal inference provides a powerful framework that can
wrap around any black-box prediction algorithm, like random forests or deep
neural networks, and generate prediction intervals with distribution-free
coverage guarantees. In this talk, I will describe how conformal inference
can be adapted to handle more complicated inferential tasks in statistics.

I will mainly focus on two important statistical problems: counterfactual
inference and time-to-event analysis. In practice, the former can be used
as a building block to infer individual treatment effects, and the latter
can be applied for individual risk assessment. Unlike standard prediction
problems, the predictive targets are only partially observable owing to
selection and censoring. When the missing data mechanism is known, as in
randomized experiments, our conformal inference-based approaches achieve
desired coverage in finite samples without any assumption on the
conditional distribution of the outcomes or the accuracy of the predictive
algorithm; when the missing data mechanism is unknown, they satisfy a
doubly robust guarantee of coverage. We demonstrate on both simulated and
real datasets that conformal inference-based methods provide more reliable
uncertainty quantification than other popular methods, which suffer from a
substantial coverage deficit even in simple models. In addition, I will
also briefly mention my work on adapting and generalizing conformal
inference to other statistical problems, including election, outlier
detection, and risk-calibrated predictions.

*Bio*: Lihua Lei <https://lihualei71.github.io/> is a postdoctoral
researcher in Statistics at Stanford University, advised by Professor
Emmanuel Candès. His current research focuses on developing rigorous
statistical methodologies for uncertainty quantification and calibration.
Prior to joining Stanford, he obtained his Ph.D. in statistics at UC
Berkeley, working on causal inference, multiple hypothesis testing, network
analysis, stochastic optimization, and econometrics.

*Host*: Dan Nicolae

*Zoom Info:*
https://uchicago.zoom.us/j/94714255217?pwd=Rm1PQ2VrRVFXTU9MZHUwV0ZXejQ4UT09
Meeting ID: 947 1425 5217
Password: ds2022


-- 
*Rob Mitchum*

*Associate Director of Communications for Data Science and Computing*
*University of Chicago*
*rmitchum at uchicago.edu <rmitchum at ci.uchicago.edu>*
*773-484-9890*
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