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Getting Started with propensity4 months ago
Setup | Basic workflow | Step 1: Fit a propensity score model | Step 2: Calculate weights and fit a weighted outcome model | Step 3: Estimate causal effects | Choosing an estimand | Handling extreme weights | Overlap estimands | Trimming | Truncation | Which approach? | Interpreting results | Binary outcomes | Continuous outcomes | Next steps | Continuous exposures | Categorical exposures | Calibration | Censoring weights | Learning more
propensity 0.1.0.9000Malcolm Barrett propensity.Rmd
An Introduction to Directed Acyclic Graphs11 months ago
Directed Acyclic Graphs | Structural Causal Graphs | Relationships between variables | Colliders and collider-stratification bias | Mediation | Resources
ggdag 0.2.13.9000Malcolm Barrettintro-to-dags.Rmd
An Introduction to ggdag11 months ago
Overview | Creating Directed Acyclic Graphs | Working with DAGs | Plotting DAGs | Plotting directly in ggplot2
ggdag 0.2.13.9000Malcolm Barrettintro-to-ggdag.Rmd
Common Structures of Bias11 months ago
Introduction | Confounders and confounding | Colliders, M-bias, and butterfly bias | Measurement error | Selection bias | Resources
ggdag 0.2.13.9000Malcolm Barrettbias-structures.Rmd
A collider example in a medical context3 years ago
Setup | Analysis | Analysis incorporating time varying data
quartets 0.1.3Lucy D'Agostino McGowan a-collider-example-in-a-medical-context.Rmd