[Sds-seminars] [Sds-announce] S&DS Talk by Lihua Lei, 2/28, 4pm, "What Can Conformal Inference Offer To Statistics?"

[elizavette.torres at yale.edu]

   <https://statistics.yale.edu/> Department of Statistics and Data Science



LIHUA LEI, Stanford University




Monday, February 28, 2022

4:00PM to 5:00PM

Via Zoom: https://yale.zoom.us/j/92599388925

 <https://lihualei71.github.io/> Website

 

Title: What Can Conformal Inference Offer To Statistics?

Information and Abstract: 

In this talk, I will describe how conformal inference can be adapted to
handle more complicated inferential tasks in statistics.  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.

 

 

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