30.2 Visualization

Before diving into estimation, it is always wise to (i) confirm the treatment pattern and (ii) eyeball the outcomes.

The panelView package offers quick heatmaps and outcome traces that make these checks painless.

30.2.1 Data check

library(panelView)
library(fixest)
library(tidyverse)
base_stagg <- fixest::base_stagg |>
    # treatment status
    dplyr::mutate(treat_stat = dplyr::if_else(time_to_treatment < 0, 0, 1)) |> 
    select(id, year, treat_stat, y)

head(base_stagg)
#>   id year treat_stat           y
#> 2 90    1          0  0.01722971
#> 3 89    1          0 -4.58084528
#> 4 88    1          0  2.73817174
#> 5 87    1          0 -0.65103066
#> 6 86    1          0 -5.33381664
#> 7 85    1          0  0.49562631

30.2.2 Treatment Assignment Heatmap

panelView::panelview(
    y ~ treat_stat,
    data = base_stagg,
    index = c("id", "year"),
    xlab = "Year",
    ylab = "Unit",
    display.all = F,
    gridOff = T,
    by.timing = T
)

Heatmap showing treatment status by unit over 10 years. The y-axis lists individual units, and the x-axis marks years. Units transition from light blue (under control) to dark blue (under treatment) at different years, illustrating staggered treatment adoption. A legend at the bottom labels the two status colors.

Figure 2.2: Treatment Assignment Over Time by Unit

The diagonal “step” confirms that not all units adopt at once. This would be a perfect for a staggered-DiD design. Horizontal segments without a color change indicate units that never adopt.

# alternatively specification
panelView::panelview(
    Y = "y",
    D = "treat_stat",
    data = base_stagg,
    index = c("id", "year"),
    xlab = "Year",
    ylab = "Unit",
    display.all = F,
    gridOff = T,
    by.timing = T
)

Heatmap of treatment status over 10 years. The x-axis shows years, and the y-axis lists individual units. Each unit transitions from light blue (under control) to dark blue (under treatment) at different points in time, forming a downward diagonal boundary that reflects staggered adoption. A legend identifies the two treatment states.

Figure 2.3: Staggered Treatment Timing Across Units

30.2.3 Raw Outcome Trajectories

# Average outcomes for each cohort
panelView::panelview(
    data = base_stagg, 
    Y = "y",
    D = "treat_stat",
    index = c("id", "year"),
    by.timing = T,
    display.all = F,
    type = "outcome", 
    by.cohort = T
)
#> Number of unique treatment histories: 10

Line plot showing individual outcome trajectories over time. Gray lines represent control units, orange lines show treated units before treatment, and red lines represent treated units after treatment. The y-axis measures outcome y, and the x-axis spans 10 years. Red lines tend to diverge upward or downward after year 5, indicating possible treatment effects.

Figure 2.4: Raw Panel Data by Treatment Status Over Time

If the red segments diverge immediately after treatment while the orange segments blend with gray beforehand, the visual evidence is supportive of a treatment effect and parallel pre-trends.

30.2.4 Event-time Averages

A more focused diagnostic is to plot the average outcome in event time (years relative to first treatment).

base_stagg |>
    group_by(event_time = year - min(year[treat_stat == 1])) |>
    summarise(y_mean = mean(y),
              se     = sd(y) / sqrt(n())) |>
    ggplot(aes(event_time, y_mean)) +
    geom_line(color = "#377eb8", linewidth = 1) +
    geom_ribbon(aes(ymin = y_mean - se, ymax = y_mean + se),
                fill = "#377eb8",
                alpha = .2) +
    geom_vline(xintercept = 0, linetype = "dashed") +
    labs(x = "Years relative to treatment",
         y = "Mean outcome (y)",
         title = "Event-time plot: do outcomes change at treatment onset?") +
    theme_minimal()

Event-study line plot showing mean outcome over event time. The horizontal axis runs from several years before to after treatment, with a dashed vertical line at zero marking treatment onset. A solid blue line traces the average outcome, and a light-blue ribbon around it depicts SE confidence bands. The viewer can compare flat pre-treatment values to any jump or slope change after zero to gauge treatment effects.

Figure 2.5: Event Time Averages

A flat pre-trend (negative event times) and a noticeable jump at event-time 0 support the identifying assumptions for staggered DiD.