R Causal Mediation Analysis

关于

This skill equips Claude with the expertise to conduct complex causal mediation analysis within the R ecosystem, supporting everything from traditional Baron-Kenny steps to modern natural effect models and comprehensive frameworks like CMAverse. It provides implementation patterns for handling binary outcomes, survival data, and multiple mediators, while emphasizing rigorous causal inference through Directed Acyclic Graph (DAG) analysis and sensitivity testing for unmeasured confounding. It is particularly useful for researchers and data scientists needing to understand the underlying mechanisms of treatment effects in clinical, social, or behavioral studies.

主要功能

• Advanced modeling of exposure-mediator interactions and sequential mediation pathways.
• Support for diverse outcome types including continuous, binary, and survival (Cox) data.
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• Implementation of traditional and modern causal mediation frameworks including mediation, CMAverse, and medflex.
• Robust sensitivity analysis tools for assessing unmeasured confounding, including E-values.
• Automated generation of causal DAGs and standardized reporting tables for research findings.

使用场景

• Decomposing treatment effects in clinical trials to understand which biological mechanisms drive patient outcomes.
• Conducting sensitivity analyses to validate the robustness of indirect effect estimates against hidden biases or unmeasured confounders.
• Analyzing complex causal chains involving multiple parallel or serial mediators in longitudinal social science research.