3 citations · 3 across the 4 of their papers we have counts for
5 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…