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Sihyung Park

dblp:420/3441 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2025
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Medical and health informatics · 50% Computational social science and digital humanities · 50%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computational social science and digital humanities
causal inference
0.912025
Evaluating and Learning Optimal Dynamic Treatment Regimes under Truncation by Death · NeurIPS 2025
Medical and health informatics › precision medicine
dynamic treatment regime
0.912025
Evaluating and Learning Optimal Dynamic Treatment Regimes under Truncation by Death · NeurIPS 2025

Methods — techniques the papers use, named apart from their topics

semiparametrically efficient estimation · 0.9multiply robust estimation · 0.9
YearPublicationVenuePosition
2025 Evaluating and Learning Optimal Dynamic Treatment Regimes under Truncation by Death
abstract
Truncation by death, a prevalent challenge in critical care, renders traditional dynamic treatment regime (DTR) evaluation inapplicable due to ill-defined potential outcomes. We introduce a principal stratification-based method, focusing on the always-survivor value function. We derive a semiparametrically efficient, multiply robust estimator for multi-stage DTRs, demonstrating its robustness and efficiency. Empirical validation and an application to electronic health records showcase its utility for personalized treatment optimization.
Sihyung Park, Wenbin Lu
NeurIPS1