Data will be analysed using causal mediation analysis with sensitivity analyses for sequential ignorability. All mediation models were specified a priori before completing data collection and without ...

Finally, it is impossible to establish causal links between the variables in this study due to its cross-sectional nature. The causal relationships between smartphone addiction, negative emotions, and ...

There are two ways to determine a causal graph: 1) expert domain knowledge and 2) causal discovery algorithms. We will focus on the former for this manufacturing application. For Causal AI to work in ...

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 ...

A causal model graph represents a network of interconnected entities and relationships, enabling the system to understand how various factors influence each other to create an optimized outcome.

Recently, Quantitative Biology published an approach entitled "Gene Regulatory Network Inference based on Causal Discovery Integrating with Graph Neural Network", that leverages graph ...

RcGNF is the R package wrapper for causal-Graphical Normalizing Flows (cGNF), a deep learning-based tool designed to answer causal questions using normalizing flows. It builds upon Graphical ...

Our foray into causal analysis is not yet complete. Until we define the methods of causal inference, we can't get to the deeper insights that causal analysis can provide.