As Large Language Models (LLMs) are widely used for tasks like document summarization, legal analysis, and medical history ...

A new study identifying 56 non-clinical risk factors associated with sudden cardiac arrest (SCA), spanning lifestyle, physical measures, psychosocial factors, socioeconomic status, and the local ...

Peng Ding, A Paradox from Randomization-Based Causal Inference, Statistical Science, Vol. 32, No. 3 (August 2017), pp. 331-345 ...

We introduce a visual analysis method for multiple causal graphs with different outcome variables, namely, multi-outcome causal graphs. Multi-outcome causal graphs are important in healthcare for ...

Recent clinical trials in oncology have used increasingly complex methodologies, such as causal inference methods for intercurrent events, external control, and covariate adjustment, posing challenges ...

Dynamic Bayesian Network (DBN) is an useful tool to learn the causal inference and social network of random variables. In this article, we analyze the correlations between the spread of coronavirus ...

We present MRPC (a PC algorithm with the principle of Mendelian Randomization), an R package that learns causal graphs with improved accuracy over existing methods.