What problem does it solve?
Bayesian analysis often becomes error-prone when models are built without a defensible causal structure, explicit priors, and rigorous diagnostics. This Skill helps you specify and validate causal assumptions with DAGs and then fit multilevel Bayesian models in R using brms, producing interpretable uncertainty-aware results.
Core Features & Use Cases
- Causal DAG specification and validation: Build a directed acyclic graph in dagitty, validate implied conditional independencies against data, and visualize the DAG for stakeholder communication.
- Bayesian regression with brms: Fit common model families (binary, counts, continuous, ordinal) with multilevel (random intercepts/slopes) structures and explicit priors.
- Uncertainty-aware interpretation: Extract posterior draws, compute posterior probabilities of direction, and derive marginal effects and predictions with marginaleffects.
- Model diagnostics and posterior checks: Assess MCMC convergence (R-hat, ESS), run posterior predictive checks, and compare prior vs posterior.
Quick Start
Fit a brms multilevel model for a binary outcome using a DAG-informed adjustment strategy, then compute marginal effects to interpret how predictors change P(outcome) with uncertainty.
