5 papers · 1 filter
TimeLAVA: Learning-Agnostic Valuation for Time Series Data
Wenqin Liu, Weizhi Quan, Aoqi Zuo +5
Data valuation quantifies the intrinsic quality of individual samples to enable principled data curation, quality control, and robust learning. For time series in critical domains…
Instrumental and Proximal Causal Inference with Gaussian Processes
Yuqi Zhang, Krikamol Muandet, Dino Sejdinovic +2
Instrumental variable (IV) and proximal causal learning (Proxy) methods are central frameworks for causal inference in the presence of unobserved confounding. Despite substantial m…
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…