Zobrazeno 1 - 10
of 482
pro vyhledávání: '"ZHAO Qingyuan"'
Autor:
Zhu, Max, Yao, Jian, Mynatt, Marcus, Pugzlys, Hubert, Li, Shuyi, Bacallado, Sergio, Zhao, Qingyuan, Jia, Chunjing
We introduce a Bayesian active learning algorithm that efficiently elucidates phase diagrams. Using a novel acquisition function that assesses both the impact and likelihood of the next observation, the algorithm iteratively determines the most infor
Externí odkaz:
http://arxiv.org/abs/2409.07042
Autor:
Zhao, Qingyuan
Directed mixed graphs permit directed and bidirected edges between any two vertices. They were first considered in the path analysis developed by Sewall Wright and play an essential role in statistical modeling. We introduce a matrix algebra for walk
Externí odkaz:
http://arxiv.org/abs/2407.15744
Off-policy evaluation (OPE) is crucial for assessing a target policy's impact offline before its deployment. However, achieving accurate OPE in large state spaces remains challenging. This paper studies state abstractions -- originally designed for p
Externí odkaz:
http://arxiv.org/abs/2406.19531
Adaptive experiments use preliminary analyses of the data to inform further course of action and are commonly used in many disciplines including medical and social sciences. Because the null hypothesis and experimental design are not pre-specified, i
Externí odkaz:
http://arxiv.org/abs/2405.07026
Many modern applications require the use of data to both select the statistical tasks and make valid inference after selection. In this article, we provide a unifying approach to control for a class of selective risks. Our method is motivated by a re
Externí odkaz:
http://arxiv.org/abs/2401.16651
Autor:
Guo, F. Richard, Zhao, Qingyuan
Confounder selection, namely choosing a set of covariates to control for confounding between a treatment and an outcome, is arguably the most important step in the design of observational studies. Previous methods, such as Pearl's celebrated back-doo
Externí odkaz:
http://arxiv.org/abs/2309.06053
Autor:
Gao, Zijun, Zhao, Qingyuan
Negative control is a common technique in scientific investigations and broadly refers to the situation where a null effect (''negative result'') is expected. Motivated by a real proteomic dataset, we will present three promising and closely connecte
Externí odkaz:
http://arxiv.org/abs/2303.01552
Autor:
Freidling, Tobias, Zhao, Qingyuan
Causal inference necessarily relies upon untestable assumptions; hence, it is crucial to assess the robustness of obtained results to violations of identification assumptions. However, such sensitivity analysis is only occasionally undertaken in prac
Externí odkaz:
http://arxiv.org/abs/2301.00040
Autor:
Zhang, Yao, Zhao, Qingyuan
Sensitivity analysis for the unconfoundedness assumption is crucial in observational studies. For this purpose, the marginal sensitivity model (MSM) gained popularity recently due to its good interpretability and mathematical properties. However, as
Externí odkaz:
http://arxiv.org/abs/2211.04697
Regression adjustment is broadly applied in randomized trials under the premise that it usually improves the precision of a treatment effect estimator. However, previous work has shown that this is not always true. To further understand this phenomen
Externí odkaz:
http://arxiv.org/abs/2210.04360