Publication-Quality Figures Skill

This skill generates figure code that meets the formatting standards of top economics journals (AER, QJE, ReStud, Econometrica, JPE). It covers the most common econometric figure types with precise control over fonts, colors, dimensions, and export formats.

Journal Requirements Summary

| Requirement | AER / QJE / ReStud Standard | |-------------|----------------------------| | File format | PDF (vector) or EPS; PNG at 300+ DPI for raster | | Dimensions | Width: 3.4in (single column) or 7in (full page); height ≤ 9in | | Font | Matching journal body font; minimum 8pt for labels | | Colors | Must be readable in grayscale; avoid red-green pairs | | Line width | ≥ 0.5pt for data lines; ≥ 0.75pt for axes | | Legend | Inside plot area or below; no box border preferred | | Notes | Figure notes below, starting with "Notes:" | | Numbering | "Figure 1:", "Figure 2:" etc. in caption |

Base Setup: Journal-Ready Defaults

Python (matplotlib)

python
# Python — journal-quality defaults
import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np

# AER/QJE style defaults
plt.rcParams.update({
    'figure.figsize': (7, 4.5),
    'figure.dpi': 300,
    'font.family': 'serif',
    'font.serif': ['Times New Roman', 'Computer Modern Roman'],
    'font.size': 11,
    'axes.labelsize': 12,
    'axes.titlesize': 13,
    'xtick.labelsize': 10,
    'ytick.labelsize': 10,
    'legend.fontsize': 10,
    'axes.linewidth': 0.8,
    'lines.linewidth': 1.5,
    'lines.markersize': 5,
    'axes.spines.top': False,
    'axes.spines.right': False,
    'savefig.bbox': 'tight',
    'savefig.pad_inches': 0.05,
})

# Grayscale-safe color palette
COLORS = ['#2c3e50', '#e74c3c', '#3498db', '#27ae60', '#f39c12', '#8e44ad']
MARKERS = ['o', 's', '^', 'D', 'v', 'P']
LINESTYLES = ['-', '--', '-.', ':', (0, (3, 1, 1, 1)), (0, (5, 2))]

R (ggplot2)

r
# R — journal-quality ggplot2 theme
library(ggplot2)
library(scales)

theme_econ <- function(base_size = 11, base_family = "serif") {
  theme_minimal(base_size = base_size, base_family = base_family) %+replace%
    theme(
      # Clean axes
      panel.grid.major = element_line(color = "grey90", linewidth = 0.3),
      panel.grid.minor = element_blank(),
      axis.line = element_line(color = "black", linewidth = 0.5),
      axis.ticks = element_line(color = "black", linewidth = 0.3),
      # Text
      axis.title = element_text(size = rel(1.1)),
      axis.text = element_text(size = rel(0.9), color = "black"),
      plot.title = element_text(size = rel(1.2), face = "bold", hjust = 0),
      plot.subtitle = element_text(size = rel(0.95), color = "grey30"),
      plot.caption = element_text(size = rel(0.8), hjust = 0, color = "grey40"),
      # Legend
      legend.position = "bottom",
      legend.title = element_blank(),
      legend.background = element_blank(),
      legend.key = element_blank(),
      # Margins
      plot.margin = margin(10, 15, 10, 10)
    )
}

# Grayscale-safe palette
econ_colors <- c("#2c3e50", "#e74c3c", "#3498db", "#27ae60", "#f39c12", "#8e44ad")

# Export function
save_econ_fig <- function(plot, filename, width = 7, height = 4.5) {
  ggsave(filename, plot, width = width, height = height, dpi = 300,
         device = cairo_pdf)  # vector PDF with embedded fonts
}

Stata

stata
* Stata — journal-quality graph scheme
set scheme s2color
graph set window fontface "Times New Roman"

* Global graph options for consistency
global graph_opts ///
    graphregion(color(white) margin(small)) ///
    plotregion(margin(medium)) ///
    ylabel(, angle(horizontal) nogrid labsize(small)) ///
    xlabel(, labsize(small)) ///
    legend(region(lcolor(none)) size(small) rows(1) position(6))

* Export as PDF
graph export "figure.pdf", as(pdf) replace

Common Econometric Figure Types

1. Event Study / Dynamic Treatment Effects

python
# Python — event study plot
import pandas as pd

def plot_event_study(coefs, ses, periods, ref_period=-1,
                     treatment_time=0, title="Event Study",
                     ylabel="Coefficient Estimate"):
    fig, ax = plt.subplots(figsize=(7, 4.5))

    # Confidence intervals
    ci_lower = [c - 1.96 * s for c, s in zip(coefs, ses)]
    ci_upper = [c + 1.96 * s for c, s in zip(coefs, ses)]

    # Plot CI band
    ax.fill_between(periods, ci_lower, ci_upper,
                    alpha=0.15, color=COLORS[0])
    # Point estimates
    ax.plot(periods, coefs, 'o-', color=COLORS[0],
            markersize=5, linewidth=1.5, zorder=5)

    # Reference lines
    ax.axhline(y=0, color='black', linewidth=0.5, linestyle='-')
    ax.axvline(x=treatment_time - 0.5, color='grey', linewidth=0.8,
               linestyle='--', alpha=0.7)

    # Labels
    ax.set_xlabel("Periods Relative to Treatment")
    ax.set_ylabel(ylabel)
    ax.set_title(title)
    ax.annotate("Treatment", xy=(treatment_time - 0.5, ax.get_ylim()[1] * 0.9),
                fontsize=9, color='grey40', ha='right')

    plt.tight_layout()
    plt.savefig("event_study.pdf")
    return fig

r
# R — event study with fixest
library(fixest)

es <- feols(y ~ i(rel_time, treat, ref = -1) | entity_id + year,
            data = df, cluster = ~entity_id)

# fixest iplot (quick)
iplot(es, xlab = "Periods Relative to Treatment",
      ylab = "Coefficient Estimate",
      main = "Event Study: Dynamic Treatment Effects")

# ggplot2 version (full control)
library(broom)
es_df <- tidy(es, conf.int = TRUE) %>%
  filter(grepl("rel_time", term)) %>%
  mutate(period = as.numeric(gsub(".*::(-?\\d+):.*", "\\1", term)))

# Add reference period
es_df <- bind_rows(es_df,
  tibble(period = -1, estimate = 0, conf.low = 0, conf.high = 0))

ggplot(es_df, aes(x = period, y = estimate)) +
  geom_ribbon(aes(ymin = conf.low, ymax = conf.high),
              fill = econ_colors[1], alpha = 0.15) +
  geom_point(color = econ_colors[1], size = 2) +
  geom_line(color = econ_colors[1], linewidth = 0.8) +
  geom_hline(yintercept = 0, linewidth = 0.5) +
  geom_vline(xintercept = -0.5, linetype = "dashed", color = "grey50") +
  labs(x = "Periods Relative to Treatment",
       y = "Coefficient Estimate",
       title = "Event Study: Dynamic Treatment Effects",
       caption = "Notes: 95% confidence intervals shown. Standard errors clustered at entity level.") +
  theme_econ()

save_econ_fig(last_plot(), "event_study.pdf")

stata
* Stata — event study plot
reghdfe y ib(-1).rel_time, absorb(entity_id year) cluster(entity_id)

coefplot, vertical drop(_cons) ///
    yline(0, lcolor(black) lwidth(thin)) ///
    xline(4.5, lcolor(gs8) lpattern(dash)) ///
    ciopts(recast(rcap) lcolor(navy) lwidth(thin)) ///
    mcolor(navy) msymbol(circle) ///
    ytitle("Coefficient Estimate") ///
    xtitle("Periods Relative to Treatment") ///
    title("Event Study: Dynamic Treatment Effects") ///
    note("Notes: 95% CIs shown. SEs clustered at entity level.") ///
    $graph_opts
graph export "event_study.pdf", as(pdf) replace

2. Coefficient Plot (Multiple Models)

python
# Python — coefficient plot comparing specifications
def plot_coefplot(models, model_names, var_names, var_labels=None):
    fig, ax = plt.subplots(figsize=(7, 0.6 * len(var_names) + 1.5))
    var_labels = var_labels or var_names
    n_models = len(models)
    offsets = np.linspace(-0.15 * (n_models-1), 0.15 * (n_models-1), n_models)

    for j, (coefs, ses, name) in enumerate(zip(
        [m['coefs'] for m in models],
        [m['ses'] for m in models],
        model_names)):

        y_pos = np.arange(len(var_names)) + offsets[j]
        ax.errorbar(coefs, y_pos, xerr=1.96 * np.array(ses),
                    fmt='o', color=COLORS[j], markersize=5,
                    capsize=3, linewidth=1.2, label=name)

    ax.axvline(x=0, color='black', linewidth=0.5)
    ax.set_yticks(range(len(var_names)))
    ax.set_yticklabels(var_labels)
    ax.set_xlabel("Coefficient Estimate")
    ax.legend(loc='lower right', frameon=False)
    ax.invert_yaxis()
    plt.tight_layout()
    plt.savefig("coefplot.pdf")

r
# R — coefficient plot with modelsummary/modelplot
library(modelsummary)

modelplot(list("OLS" = m1, "IV" = m2, "FE" = m3),
          coef_map = c("x1" = "Treatment", "x2" = "Income", "x3" = "Education"),
          color = "model") +
  geom_vline(xintercept = 0, linetype = "dashed") +
  labs(x = "Coefficient Estimate", y = "") +
  scale_color_manual(values = econ_colors[1:3]) +
  theme_econ()

3. Binned Scatter Plot (binscatter)

python
# Python — binned scatter with linear fit
def plot_binscatter(x, y, n_bins=20, controls=None, xlabel="X", ylabel="Y",
                    title="", residualize=False):
    import statsmodels.api as sm

    if residualize and controls is not None:
        # Residualize both x and y on controls
        x = sm.OLS(x, sm.add_constant(controls)).fit().resid
        y = sm.OLS(y, sm.add_constant(controls)).fit().resid

    # Create bins
    bins = pd.qcut(x, n_bins, duplicates='drop')
    bin_means = pd.DataFrame({'x': x, 'y': y, 'bin': bins}).groupby('bin').mean()

    fig, ax = plt.subplots(figsize=(7, 4.5))
    ax.scatter(bin_means['x'], bin_means['y'], color=COLORS[0],
               s=40, zorder=5, edgecolors='white', linewidth=0.5)

    # Linear fit
    z = np.polyfit(bin_means['x'], bin_means['y'], 1)
    x_line = np.linspace(bin_means['x'].min(), bin_means['x'].max(), 100)
    ax.plot(x_line, np.polyval(z, x_line), color=COLORS[1],
            linewidth=1.2, linestyle='--')

    slope, pval = z[0], sm.OLS(y, sm.add_constant(x)).fit().pvalues[1]
    ax.annotate(f"Slope = {slope:.3f} (p = {pval:.3f})",
                xy=(0.05, 0.95), xycoords='axes fraction',
                fontsize=10, va='top')

    ax.set_xlabel(xlabel)
    ax.set_ylabel(ylabel)
    ax.set_title(title)
    plt.tight_layout()
    plt.savefig("binscatter.pdf")

r
# R — binscatter with binsreg (Cattaneo et al.)
library(binsreg)
binsreg(y = df$y, x = df$x, w = ~ x1 + x2,  # controls
        data = df, dots = c(0, 0), line = c(3, 3),
        ci = c(3, 3), cb = c(3, 3),
        title = "Binned Scatter: Y vs X",
        x.label = "X Variable", y.label = "Y Variable")

# ggplot2 manual version
library(dplyr)
df_binned <- df %>%
  mutate(bin = ntile(x, 20)) %>%
  group_by(bin) %>%
  summarize(x = mean(x), y = mean(y))

ggplot(df_binned, aes(x = x, y = y)) +
  geom_point(color = econ_colors[1], size = 3) +
  geom_smooth(method = "lm", se = FALSE, color = econ_colors[2],
              linetype = "dashed", linewidth = 0.8) +
  labs(x = "X Variable", y = "Y Variable") +
  theme_econ()

stata
* Stata — binscatter
ssc install binscatter

binscatter y x, controls(x1 x2) nquantiles(20) ///
    lcolor(navy) mcolor(navy) ///
    ytitle("Y Variable") xtitle("X Variable") ///
    title("Binned Scatter: Y vs X") ///
    $graph_opts
graph export "binscatter.pdf", as(pdf) replace

4. RDD Visualization

python
# Python — RDD plot with local polynomial fit
def plot_rdd(running_var, outcome, cutoff, bandwidth=None,
             n_bins=20, title="Regression Discontinuity"):
    fig, ax = plt.subplots(figsize=(7, 4.5))

    # Binned means (separate for each side)
    for side, mask, color in [("Control", running_var < cutoff, COLORS[0]),
                               ("Treated", running_var >= cutoff, COLORS[1])]:
        x_side = running_var[mask]
        y_side = outcome[mask]
        bins = pd.qcut(x_side, min(n_bins, len(x_side)//5), duplicates='drop')
        bm = pd.DataFrame({'x': x_side, 'y': y_side, 'bin': bins}).groupby('bin').mean()
        ax.scatter(bm['x'], bm['y'], color=color, s=35, zorder=5,
                   edgecolors='white', linewidth=0.5)

        # Local polynomial fit
        z = np.polyfit(x_side, y_side, 1)
        x_fit = np.linspace(x_side.min(), x_side.max(), 200)
        ax.plot(x_fit, np.polyval(z, x_fit), color=color, linewidth=1.5)

    # Cutoff line
    ax.axvline(x=cutoff, color='grey', linewidth=1, linestyle='--')
    ax.set_xlabel("Running Variable")
    ax.set_ylabel("Outcome")
    ax.set_title(title)

    if bandwidth:
        ax.axvspan(cutoff - bandwidth, cutoff + bandwidth,
                   alpha=0.05, color='grey')

    plt.tight_layout()
    plt.savefig("rdd_plot.pdf")

r
# R — RDD plot (rdrobust)
library(rdrobust)
rdplot(y = df$y, x = df$running_var, c = cutoff,
       title = "Regression Discontinuity",
       x.label = "Running Variable", y.label = "Outcome",
       col.dots = econ_colors[1:2], col.lines = econ_colors[1:2])

# ggplot2 version
ggplot(df, aes(x = running_var, y = y)) +
  geom_point(aes(color = running_var >= cutoff), alpha = 0.15, size = 1) +
  geom_smooth(data = filter(df, running_var < cutoff),
              method = "lm", formula = y ~ poly(x, 1),
              color = econ_colors[1], fill = econ_colors[1], alpha = 0.1) +
  geom_smooth(data = filter(df, running_var >= cutoff),
              method = "lm", formula = y ~ poly(x, 1),
              color = econ_colors[2], fill = econ_colors[2], alpha = 0.1) +
  geom_vline(xintercept = cutoff, linetype = "dashed", color = "grey40") +
  scale_color_manual(values = econ_colors[1:2], guide = "none") +
  labs(x = "Running Variable", y = "Outcome",
       title = "Regression Discontinuity Design") +
  theme_econ()

stata
* Stata — RDD plot
rdplot y running_var, c(cutoff) ///
    graph_options(title("Regression Discontinuity") ///
    ytitle("Outcome") xtitle("Running Variable") ///
    $graph_opts)
graph export "rdd_plot.pdf", as(pdf) replace

5. Kernel Density / Distribution Comparison

python
# Python — overlapping density plot
from scipy.stats import gaussian_kde

def plot_density(groups, labels, xlabel="Value", title=""):
    fig, ax = plt.subplots(figsize=(7, 4.5))
    for i, (data, label) in enumerate(zip(groups, labels)):
        kde = gaussian_kde(data, bw_method='silverman')
        x_grid = np.linspace(data.min() - data.std(), data.max() + data.std(), 300)
        ax.plot(x_grid, kde(x_grid), color=COLORS[i], linewidth=1.5, label=label)
        ax.fill_between(x_grid, kde(x_grid), alpha=0.1, color=COLORS[i])
    ax.set_xlabel(xlabel)
    ax.set_ylabel("Density")
    ax.set_title(title)
    ax.legend(frameon=False)
    plt.tight_layout()
    plt.savefig("density.pdf")

r
# R — density comparison
ggplot(df, aes(x = outcome, fill = group, color = group)) +
  geom_density(alpha = 0.15, linewidth = 0.8) +
  scale_fill_manual(values = econ_colors[1:2]) +
  scale_color_manual(values = econ_colors[1:2]) +
  labs(x = "Outcome", y = "Density",
       title = "Distribution by Group") +
  theme_econ()

stata
* Stata — density comparison
twoway (kdensity outcome if group == 0, lcolor(navy) lwidth(medthick)) ///
       (kdensity outcome if group == 1, lcolor(cranberry) lwidth(medthick) ///
        lpattern(dash)), ///
       legend(label(1 "Control") label(2 "Treatment")) ///
       ytitle("Density") xtitle("Outcome") ///
       title("Distribution by Group") ///
       $graph_opts
graph export "density.pdf", as(pdf) replace

6. Time Series / Trend Plot

python
# Python — time series with shaded recession bars
def plot_timeseries(dates, series_dict, recessions=None,
                    ylabel="", title=""):
    fig, ax = plt.subplots(figsize=(7, 4.5))
    for i, (label, values) in enumerate(series_dict.items()):
        ax.plot(dates, values, color=COLORS[i], linewidth=1.5,
                linestyle=LINESTYLES[i], label=label)

    if recessions:
        for start, end in recessions:
            ax.axvspan(start, end, alpha=0.08, color='grey')

    ax.set_ylabel(ylabel)
    ax.set_title(title)
    ax.legend(frameon=False, loc='best')
    fig.autofmt_xdate()
    plt.tight_layout()
    plt.savefig("timeseries.pdf")

r
# R — time series with recession shading
library(ggplot2)

ggplot(df, aes(x = date, y = value)) +
  geom_rect(data = recessions,
            aes(xmin = start, xmax = end, ymin = -Inf, ymax = Inf),
            inherit.aes = FALSE, fill = "grey", alpha = 0.1) +
  geom_line(aes(color = series, linetype = series), linewidth = 0.8) +
  scale_color_manual(values = econ_colors) +
  labs(x = "", y = "Value", title = "Time Series Comparison") +
  theme_econ()

7. Synthetic Control Gap Plot

python
# Python — treated vs synthetic control + gap
def plot_synth(years, treated, synthetic, treatment_year, title=""):
    fig, axes = plt.subplots(1, 2, figsize=(12, 4.5))

    # Panel A: Levels
    ax = axes[0]
    ax.plot(years, treated, color=COLORS[0], linewidth=1.5, label="Treated")
    ax.plot(years, synthetic, color=COLORS[1], linewidth=1.5,
            linestyle='--', label="Synthetic Control")
    ax.axvline(x=treatment_year, color='grey', linewidth=0.8, linestyle='--')
    ax.set_title("(a) Treated vs. Synthetic Control")
    ax.set_ylabel("Outcome")
    ax.legend(frameon=False)

    # Panel B: Gap
    ax = axes[1]
    gap = np.array(treated) - np.array(synthetic)
    ax.plot(years, gap, color=COLORS[0], linewidth=1.5)
    ax.fill_between(years, 0, gap, where=np.array(years) >= treatment_year,
                    alpha=0.15, color=COLORS[0])
    ax.axhline(y=0, color='black', linewidth=0.5)
    ax.axvline(x=treatment_year, color='grey', linewidth=0.8, linestyle='--')
    ax.set_title("(b) Treatment Effect (Gap)")
    ax.set_ylabel("Treated − Synthetic")

    for ax in axes:
        ax.set_xlabel("Year")
    plt.tight_layout()
    plt.savefig("synth_plot.pdf")

8. Multi-Panel Figures

python
# Python — multi-panel layout
fig, axes = plt.subplots(2, 2, figsize=(12, 9))

for i, (ax, data, title) in enumerate(zip(
    axes.flat, datasets, panel_titles)):
    ax.scatter(data['x'], data['y'], color=COLORS[0], s=20, alpha=0.5)
    ax.set_title(f"({chr(97+i)}) {title}", fontsize=11)
    ax.set_xlabel("X")
    ax.set_ylabel("Y")

plt.tight_layout()
plt.savefig("multipanel.pdf")

r
# R — multi-panel with patchwork
library(patchwork)

p1 <- ggplot(df, aes(x, y1)) + geom_point(size = 1, alpha = 0.3) +
  labs(title = "(a) Panel A") + theme_econ()
p2 <- ggplot(df, aes(x, y2)) + geom_point(size = 1, alpha = 0.3) +
  labs(title = "(b) Panel B") + theme_econ()
p3 <- ggplot(df, aes(x, y3)) + geom_line() +
  labs(title = "(c) Panel C") + theme_econ()
p4 <- ggplot(df, aes(x, y4)) + geom_line() +
  labs(title = "(d) Panel D") + theme_econ()

combined <- (p1 | p2) / (p3 | p4)
save_econ_fig(combined, "multipanel.pdf", width = 12, height = 9)

stata
* Stata — multi-panel with graph combine
graph combine panel_a panel_b panel_c panel_d, ///
    rows(2) cols(2) ///
    title("Figure 1: Main Results") ///
    $graph_opts
graph export "multipanel.pdf", as(pdf) replace

Formatting Checklist

Before submitting to a journal, verify:

• ] Vector format: Exported as PDF (not PNG/JPG) for line plots
• ] Readable in grayscale: Print in B&W to check
• ] Font consistency: Same font family as paper body text
• ] Axis labels: Descriptive, with units (e.g., "Income (1000 USD)")
• ] No chartjunk: Remove gridlines, borders, and unnecessary decoration
• ] Proper aspect ratio: Not stretched or compressed
• ] Legend placement: Inside plot if space allows; below otherwise
• ] Panel labels: (a), (b), (c) for multi-panel figures
• ] Notes below figure: Data source, sample, key definitions
• ] CI/SE shown: For any estimated quantities (coefficients, treatment effects)
• ] Reference lines: Zero line for coefficient plots; cutoff for RDD; treatment date for event study

Common Pitfalls

• Using default matplotlib/ggplot themes: They look unprofessional — always customize
• Raster exports for line plots: Use PDF/EPS, not PNG, for any plot with lines or text
• Too many colors: Limit to 3–4 distinguishable colors; use linestyle for additional series
• Tiny axis labels: Minimum 8pt after scaling to final size in the paper
• Missing confidence intervals: Never show point estimates without uncertainty
• 3D plots: Almost never appropriate in economics — use 2D alternatives
• Pie charts: Never use in academic economics papers

Output File Management

Consistent figure naming and directory layout prevents the most common assembly problem: the paper's \includegraphics{} calls pointing to files that don't exist or are scattered across subdirectories (data/bartik/, data/results/, output/, etc.).

Standard Convention

Save all figures to a single figures/ directory at the project root. Use zero-padded sequential prefixes:

figures/
  fig01_aaei_distribution.pdf
  fig02_parallel_trends.pdf
  fig03_event_study_wages.pdf
  fig04_event_study_employment.pdf
  fig05_heterogeneity_wage_group.pdf
  ...

The numeric prefix (fig01_, fig02_, ...) makes insertion order explicit and survives alphabetical sorting. The descriptive suffix means you can identify the figure without opening it.

Python Helper

Add this to any figure-generating script to enforce the convention:

python
import os, matplotlib.pyplot as plt

FIGURES_DIR = "figures"
os.makedirs(FIGURES_DIR, exist_ok=True)

def save_fig(fig, fig_num: int, name: str, formats=("pdf",)):
    """Save to figures/figNN_name.ext with consistent naming."""
    for fmt in formats:
        path = os.path.join(FIGURES_DIR, f"fig{fig_num:02d}_{name}.{fmt}")
        fig.savefig(path, bbox_inches='tight', dpi=300)
        print(f"Saved: {path}")
    return path

# Usage:
fig, ax = plt.subplots(figsize=(7, 4.5))
# ... plotting code ...
save_fig(fig, fig_num=3, name="event_study_wages")

R Helper

r
FIGURES_DIR <- "figures"
dir.create(FIGURES_DIR, showWarnings = FALSE, recursive = TRUE)

save_fig <- function(plot, fig_num, name, width = 7, height = 4.5) {
  path <- file.path(FIGURES_DIR, sprintf("fig%02d_%s.pdf", fig_num, name))
  ggsave(path, plot, width = width, height = height, device = cairo_pdf)
  cat("Saved:", path, "\n")
  invisible(path)
}

# Usage:
p <- ggplot(...) + theme_econ()
save_fig(p, fig_num = 3, name = "event_study_wages")

In the LaTeX Paper

latex
% In preamble — point to figures/ once:
\graphicspath{{../figures/}}

% In the paper body — no path needed in each call:
\begin{figure}[htbp]
  \centering
  \includegraphics[width=0.9\textwidth]{fig03_event_study_wages}
  \caption{Event Study: Effect of AI Exposure on Log Wages}
  \label{fig:event_study}
\end{figure}

When multiple scripts generate figures (e.g., main analysis, robustness, heterogeneity), add a comment block at the top of each script listing which figure numbers it produces. This prevents two scripts overwriting the same file.

Related Skills & Commands

• /plot: Command that generates visualization code for specific analyses
• stats: Summary statistics that inform what to visualize
• table: Companion tables that present the same results numerically
• did-analysis: Event study plots are the key figure for DID papers
• rdd-analysis: RDD binned scatter is essential for discontinuity papers
• synthetic-control: Gap plots and placebo plots for SCM papers