Abstract: This paper presents a method to integrate causal inference into deep learning for time series forecasting. We consider time series for complex systems characterized by non-linear dynamics, ...

Abstract: Causal inference with spatial, temporal, and meta-analytic data commonly defaults to regression modeling. While widely accepted, such regression approaches can suffer from model ...

Abstract: Causal inference and root cause analysis play a crucial role in network performance evaluation and optimization by identifying critical parameters and explaining how the configuration ...

What is this book about? Causal methods present unique challenges compared to traditional machine learning and statistics. Learning causality can be challenging, but it offers distinct advantages that ...

Background: Traditional congenital heart surgery quality assessments rely on indirect standardization via regression, which can be complicated by heterogeneity in case-mix, surgical volume, and low ...

ABSTRACT: Special education services are designed to provide tailored support for students with diverse learning needs, with the expectation of improving academic achievement. This study examines the ...

Please join the Department of Epidemiology Center for Clinical Trials and Evidence Synthesis (CCTES) and Center for Drug Safety and Effectiveness (CDSE) in welcoming Elizabeth Stuart, PhD, AM, Chair ...

oLLM is a lightweight Python library built on top of Huggingface Transformers and PyTorch and runs large-context Transformers on NVIDIA GPUs by aggressively offloading weights and KV-cache to fast ...

Join the ORBIT (Observational Research Building Interdisciplinary Therapeutic Advances) Interdisciplinary Hub for their inaugural seminar "Principles of Causal Inference Using Observational Data" on ...

Gary Seidman profiles Stanford economist Guido Imbens, who is reshaping how researchers establish cause and effect in the real world So Imbens and his colleagues designed and tested sharper tools to ...