Matthew Kosko

MBD: A Model-Based Debiasing Framework Across User, Content, and Model Dimensions

Yuantong Li, Lei Yuan, Zhihao Zheng, Weimiao Wu et autres

Modern recommendation systems rank candidates by aggregating multiple behavioral signals through a value model. However, many commonly used signals are inherently affected by heterogeneous biases. For example, watch time naturally favors long-form content, loop rate favors short - form content, and comment …

Fast Uncertainty Quantification for Kernel-Based Estimators in Large-Scale Causal Inference

Matthew Kosko, Falco J, Bargagli-Stoffi, Lin Wang et autres

Kernel methods are widely used in causal inference for tasks such as treatment effect estimation, policy evaluation, and policy learning. The bootstrap is a standard tool for uncertainty quantification because of its broad applicability. As increasingly large datasets become available, such as …

A fast bootstrap algorithm for causal inference with large data

Matthew Kosko, Lin Wang, Michele Santacatterina

Estimating causal effects from large experimental and observational data has become increasingly prevalent in both industry and research. The bootstrap is an intuitive and powerful technique used to construct standard errors and confidence intervals of estimators. Its application however can be prohibitively …

us (code pays fourni par la source)

A Fast Bootstrap Algorithm for Causal Inference with Large Data

Matthew Kosko, Lin Wang, Michele Santacatterina

Estimating causal effects from large experimental and observational data has become increasingly prevalent in both industry and research. The bootstrap is an intuitive and powerful technique used to construct standard errors and confidence intervals of estimators. Its application however can be prohibitively …