Plot Treatment vs Control Group Comparisons

Creates comprehensive pre- and post-treatment outcome comparisons between treatment and control groups. Shows means, distributions, and change patterns for impact evaluation visualization.

Usage

plot_treatment_control_comparison(
  data,
  outcome_vars,
  treatment_var = "is_treated",
  time_var = "post_treatment",
  id_var = "cf",
  comparison_type = "means",
  use_bw = FALSE,
  show_ci = TRUE,
  ci_level = 0.95,
  dodge_width = 0.8,
  error_bar_width = 0.2,
  alpha = 0.7,
  ncol = 2
)

Arguments

data

Data.table. Panel data with treatment/control groups and outcomes

outcome_vars

Character vector. Outcome variables to compare

treatment_var

Character. Treatment indicator variable. Default: "is_treated"

time_var

Character. Time variable (pre/post). Default: "post_treatment"

id_var

Character. Individual identifier. Default: "cf"

comparison_type

Character. Type of comparison: "means", "distributions", "changes", "all". Default: "means"

use_bw

Logical. Use black and white theme. Default: FALSE

show_ci

Logical. Show confidence intervals. Default: TRUE

ci_level

Numeric. Confidence level for intervals. Default: 0.95

dodge_width

Numeric. Dodge width for grouped bars. Default: 0.8

error_bar_width

Numeric. Width of error bar caps. Default: 0.2

alpha

Numeric. Transparency for distributions. Default: 0.7

ncol

Integer. Number of columns for faceted plots. Default: 2

Value

A ggplot2 object or list of plots (when comparison_type = "all")

See also

plot_balance_assessment, plot_did_results

Examples

if (FALSE) { # \dontrun{
# Basic means comparison
comparison_plot <- plot_treatment_control_comparison(
  data = panel_data,
  outcome_vars = c("employment_rate", "avg_wage"),
  comparison_type = "means"
)

# Distribution comparisons
dist_plots <- plot_treatment_control_comparison(
  data = panel_data,
  outcome_vars = "job_stability",
  comparison_type = "distributions",
  use_bw = TRUE
)
} # }