4 papers
Uncertainty Propagation in LLM-Based Systems
Boming Xia, Liming Zhu, Erdun Gao +3
Uncertainty in large language model (LLM)-based systems is often studied at the level of a single model output, yet deployed LLM applications are compound systems in which uncertai…
Observationally Informed Adaptive Causal Experimental Design
Erdun Gao, Liang Zhang, Jake Fawkes +5
Randomized Controlled Trials (RCTs) represent the gold standard for causal inference yet remain a scarce resource. While large-scale observational data is often available, it is ut…
ActiveCQ: Active Estimation of Causal Quantities
Erdun Gao, Dino Sejdinovic
Estimating causal quantities (CQs) typically requires large datasets, which can be expensive to obtain, especially when measuring individual outcomes is costly. This challenge high…
Causal-EPIG: A Prediction-Oriented Active Learning Framework for CATE Estimation
Erdun Gao, Jake Fawkes, Dino Sejdinovic
Estimating the Conditional Average Treatment Effect (CATE) is often constrained by the high cost of obtaining outcome measurements, making active learning essential. However, conve…