Zobrazeno 1 - 10
of 80
pro vyhledávání: '"Li Michael Lingzhi"'
Autor:
Li Michael Lingzhi, Imai Kosuke
Publikováno v:
Journal of Causal Inference, Vol 12, Iss 1, Pp 1-51 (2024)
A century ago, Neyman showed how to evaluate the efficacy of treatment using a randomized experiment under a minimal set of assumptions. This classical repeated sampling framework serves as a basis of routine experimental analyses conducted by today
Externí odkaz:
https://doaj.org/article/75dfd8983031468dbaa32e87762a573c
Autor:
Li, Michael Lingzhi, Zhu, Shixiang
The surge in data availability has inundated decision-makers with an overwhelming array of choices. While existing approaches focus on optimizing decisions based on quantifiable metrics, practical decision-making often requires balancing measurable q
Externí odkaz:
http://arxiv.org/abs/2409.11535
We define an online learning and optimization problem with irreversible decisions contributing toward a coverage target. At each period, a decision-maker selects facilities to open, receives information on the success of each one, and updates a machi
Externí odkaz:
http://arxiv.org/abs/2406.14777
Autor:
Li, Michael Lingzhi, Imai, Kosuke
A century ago, Neyman showed how to evaluate the efficacy of treatment using a randomized experiment under a minimal set of assumptions. This classical repeated sampling framework serves as a basis of routine experimental analyses conducted by today'
Externí odkaz:
http://arxiv.org/abs/2404.17019
We introduce the "cram" method, a general and efficient approach to simultaneous learning and evaluation using a generic machine learning (ML) algorithm. In a single pass of batched data, the proposed method repeatedly trains an ML algorithm and test
Externí odkaz:
http://arxiv.org/abs/2403.07031
Predictive contagion models are ubiquitous in epidemiology, social sciences, engineering, and management. This paper formulates a prescriptive contagion analytics model where a decision-maker allocates shared resources across multiple segments of a p
Externí odkaz:
http://arxiv.org/abs/2310.14559
Autor:
Li, Michael Lingzhi, Imai, Kosuke
Across a wide array of disciplines, many researchers use machine learning (ML) algorithms to identify a subgroup of individuals who are likely to benefit from a treatment the most (``exceptional responders'') or those who are harmed by it. A common a
Externí odkaz:
http://arxiv.org/abs/2310.07973
Autor:
Liu, Junling, Zhou, Peilin, Hua, Yining, Chong, Dading, Tian, Zhongyu, Liu, Andrew, Wang, Helin, You, Chenyu, Guo, Zhenhua, Zhu, Lei, Li, Michael Lingzhi
Recent advancements in large language models (LLMs) have transformed the field of question answering (QA). However, evaluating LLMs in the medical field is challenging due to the lack of standardized and comprehensive datasets. To address this gap, w
Externí odkaz:
http://arxiv.org/abs/2306.03030
We consider the estimation of average treatment effects in observational studies and propose a new framework of robust causal inference with unobserved confounders. Our approach is based on distributionally robust optimization and proceeds in two ste
Externí odkaz:
http://arxiv.org/abs/2210.08326
Autor:
Imai, Kosuke, Li, Michael Lingzhi
Researchers are increasingly turning to machine learning (ML) algorithms to investigate causal heterogeneity in randomized experiments. Despite their promise, ML algorithms may fail to accurately ascertain heterogeneous treatment effects under practi
Externí odkaz:
http://arxiv.org/abs/2203.14511