Parallel Trends Plot for DiD — parallel_trends

Creates a publication-ready time-series plot comparing treated and control group mean outcomes before and after treatment. Pre-treatment parallelism is annotated visually to help assess the key identifying assumption of difference-in-differences designs.

Usage

parallel_trends_plot(
  data,
  unit_var,
  time_var,
  outcome_var,
  treatment_var,
  treat_time,
  title = NULL
)

Arguments

data: A data frame in long (panel) format.
unit_var: Character. Name of the unit/panel identifier column.
time_var: Character. Name of the time period column (numeric or integer).
outcome_var: Character. Name of the outcome variable column (numeric).
treatment_var: Character. Name of the binary treatment indicator column (0/1 or logical). Treatment status is taken as the ever-treated indicator (max value per unit).
treat_time: Numeric. The first treatment period (vertical line placement and shading boundary).
title: Character or NULL. Plot title. Default NULL uses a generic title.

Value

A ggplot2 object.

Details

Parallel Trends Visualization for Difference-in-Differences

Plots mean outcome trajectories for treated and control groups over time, with shaded pre- and post-treatment regions, 95% confidence interval ribbons, a vertical dashed line at the treatment period, and a visual annotation highlighting the pre-treatment parallel trends assumption.

References

Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics. Princeton University Press.

Examples

if (requireNamespace("fixest", quietly = TRUE)) {
  data("base_stagg", package = "fixest")
  parallel_trends_plot(
    data          = base_stagg,
    unit_var      = "id",
    time_var      = "year",
    outcome_var   = "y",
    treatment_var = "treated",
    treat_time    = 0,
    title         = "Parallel Trends: base_stagg Data"
  )
}