BMJ Open

Introduction to secret sharing based secure multiparty computation in health data

Background Joint analyses across multiple health datasets can increase statistical power and improve the generalisability of ...
IEEE

Causal Counterfactual Inference Network for Video Object State Changes in Open-World Scenarios

Abstract: Object state changes (OSCs) play a critical role in video understanding, as they focus on localizing the stages of state transitions within temporal sequences. However, existing methods face ...
GitHub

Causal Inference and Discovery in Python

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 ...
EurekAlert!

New approach to multi-hop question answering enhances robustness using causal inference

Beijing, China — Researchers from Beijing Institute of Technology and other leading institutions have developed a novel approach for improving Multi-Hop Question Answering (MHQA) tasks, which require ...
BMJ

Dose-response interplay between light and moderate-to-vigorous physical activity on all-cause mortality risk: a causal inference analysis

Objective We employed a causal inference framework to estimate the counterfactual dose-response effects of light-intensity physical activity (LPA) on mortality across low, medium and high moderate- to ...
IEEE

Variational GAN Enhanced Causal Effect Inference for Interpretable Learning in Heterogeneous IoT Systems

Abstract: While data-driven learning techniques are revolutionizing the smart application design and development, fundamental issues of lacking generalizability and interpretability are posing great ...
blockchain

Karpathy Releases Minimal GPT: Train and Inference in 243 Lines of Pure Python — Latest Analysis and Business Implications

According to Andrej Karpathy on X, he released a 243-line, dependency-free Python implementation that can both train and run a GPT model, presenting the full algorithmic content without external ...
jech.bmj

Descriptive estimands, causal estimands, and avoiding the jungle of adjusted associations ‘in between’

2 Max Planck - University of Helsinki Center for Social Inequalities in Population Health, Helsinki, Finland Be upfront about the research study’s intention (this should link directly to the aim) - is ...