Abstract Number:
2419
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Yi Liu (1), Alexander Levis (2), Sharon-Lise Normand (3), Larry Han (4)
Institutions:
(1) North Carolina State University, N/A, (2) Carnegie Mellon University, N/A, (3) Harvard Medical School, N/A, (4) Northeastern University, N/A
Co-Author(s):
First Author:
Yi Liu
North Carolina State University
Presenting Author:
Abstract Text:
Recent years have seen a growing utilization of machine learning models to inform high-stakes decision-making. However, distribution shifts and privacy concerns make it challenging to achieve valid inferences in multi-source environments. We generate distribution-free prediction intervals for a target population, leveraging multiple potentially biased data sources. We derive the efficient influence functions for the quantiles of unobserved outcomes and show that one can incorporate machine learning prediction algorithms in the estimation of nuisance functions while still achieving parametric rates of convergence. Moreover, when conditional outcome invariance is violated, we propose a data-adaptive strategy to weight data sources to balance efficiency gain and bias reduction. We highlight the robustness and efficiency of our proposals for a variety of conformal scores and data-generating mechanisms via extensive synthetic experiments and real data analyses.
Keywords:
Conformal prediction|Distribution shift|Federated learning|Missing data|Machine learning|Data integration
Sponsors:
IMS
Tracks:
Statistical Methodology
Can this be considered for alternate subtype?
No
Are you interested in volunteering to serve as a session chair?
No
I have read and understand that JSM participants must abide by the Participant Guidelines.
Yes
I understand that JSM participants must register and pay the appropriate registration fee by June 1, 2024. The registration fee is non-refundable.
I understand