3 citations · 5 across the 9 of their papers we have counts for
10 papers
Counterfactual Optimization of Policy Interventions: Lexical Ordering and Leapfrogging
Martina Scauda, Tobias Freidling, Qingyuan Zhao
Most data-driven policy learning methods maximize average outcomes, overlooking the possibility that a policy beneficial on average may still harm a substantial fraction of individ…
On statistical and causal models associated with acyclic directed mixed graphs
Qingyuan Zhao
Causal models in statistics are often described using acyclic directed mixed graphs (ADMGs), which contain directed and bidirected edges and no directed cycles. This article survey…
A Graphical Approach to State Variable Selection in Off-policy Learning
Joakim Blach Andersen, Qingyuan Zhao
Sequential decision problems are widely studied across many areas of science. A key challenge when learning policies from historical data - a practice commonly referred to as off-p…
Counterfactual explainability and analysis of variance
Zijun Gao, Qingyuan Zhao
Existing tools for explaining complex models and systems are associational rather than causal and do not provide mechanistic understanding. We propose a new notion called counterfa…
Active Learning for Discovering Complex Phase Diagrams with Gaussian Processes
Max Zhu, Jian Yao, Marcus Mynatt +5
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…
A matrix algebra for graphical statistical models
Qingyuan Zhao
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 essentia…
Off-policy Evaluation with Deeply-abstracted States
Meiling Hao, Pingfan Su, Liyuan Hu +3
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 challengi…
Selective Randomization Inference for Adaptive Experiments
Tobias Freidling, Qingyuan Zhao, Zijun Gao
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. Becaus…
A constructive approach to selective risk control
Zijun Gao, Wenjie Hu, Qingyuan Zhao
Many modern applications require using data to select the statistical tasks and make valid inference after selection. In this article, we provide a unifying approach to control for…
Confounder selection via iterative graph expansion
F. Richard Guo, Qingyuan Zhao
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 an obs…