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

Writing YAML with ymlthis | Parameterized reports | References and citations | R Markdown extensions | R Markdown workflows | YAML add-in | Writing to files | Placing YAML on your clipboard | Working with yml objects

Directed Acyclic Graphs | Structural Causal Graphs | Relationships between variables | Colliders and collider-stratification bias | Mediation | Resources

Overview | Creating Directed Acyclic Graphs | Working with DAGs | Plotting DAGs | Plotting directly in ggplot2

Introduction | Confounders and confounding | Colliders, M-bias, and butterfly bias | Measurement error | Selection bias | Resources

Customizing existing partitioners | Creating new partitioners | partition_step(), map_cluster(), and reduce_cluster()

Introduction to the partition package | Reducing data with partition | The partition algorithm | Direct-Measure-Reduce and Partitioners | Permutation tests | References

Basic YAML | LaTeX/PDF Options | R Markdown Websites | Citations | blogdown YAML | bookdown YAML | pkgdown YAML | pagedown YAML | distill YAML | rticles YAML | RStudio Connect Scheduled Email YAML

Basic syntax | Types in YAML | Sources of YAML

Creating plots | Customizing plots | Summarizing confounder control | Combining with forest plots | Using control quality as an analytic category | Visualizing non-confounders

Estimating for precision | Estimating Precision for a Sample Size | Probability that upper limit is below level of concern | Mapping and Plotting with precisely