fixes

Overview

fixes is an R package for event study analysis in panel data — the workhorse tool for visualizing parallel trends and dynamic treatment effects in difference-in-differences research.

Version 0.8.0 adds three modern estimators designed for staggered adoption (where different units adopt treatment at different times), all accessible through the same run_es() interface.

Key functions:

Estimators (selected via the estimator argument in run_es()):

Installation

# From CRAN
install.packages("fixes")

# Development version
pak::pak("yo5uke/fixes")

Quick start

library(fixes)

All four estimators share the same interface:

# Classic TWFE (single treatment date)
res_twfe <- run_es(
  data = df,
  outcome = y,
  treatment = treat,
  time = period,
  timing = 5,
  fe = ~ id + period
)

# Sun & Abraham (2021) — staggered adoption
res_sa <- run_es(
  data = df_stagg,
  outcome = y,
  time = year,
  timing = timing,
  unit = id,
  fe = ~ id + year,
  staggered = TRUE,
  estimator = "sa"
)

# Callaway & Sant'Anna (2021) — staggered adoption
res_cs <- run_es(
  data = df_stagg,
  outcome = y,
  time = year,
  timing = timing,
  unit = id,
  staggered = TRUE,
  estimator = "cs",
  control_group = "nevertreated"
)

# Borusyak, Jaravel & Spiess (2024) — staggered adoption
res_bjs <- run_es(
  data = df_stagg,
  outcome = y,
  time = year,
  timing = timing,
  unit = id,
  staggered = TRUE,
  estimator = "bjs"
)

Classic event study (single treatment date)

Use run_es() with a fixed event date. Here we use fixest::base_did, a simulated balanced panel where all units are treated at period 5.

df <- fixest::base_did

es <- run_es(
  data = df,
  outcome = y, # outcome variable (unquoted)
  treatment = treat, # 0/1 treatment indicator
  time = period, # time variable
  timing = 5, # treatment occurs at period 5
  fe = ~ id + period,
  cluster = ~id,
  baseline = -1 # period -1 is the reference (default)
)

print(es)

## Event Study Result (fixes)
##   N: 1080  | Units: NA  | Treated units: 1080  | Never-treated: NA 
##   FE: id + period
##   VCOV: HC1  | Cluster: id 
##   Method: classic  | lead_range: 4  lag_range: 5  baseline: -1

plot_es(es)

Staggered adoption

When units adopt treatment at different times, the classic TWFE estimator can be biased. fixes provides three modern alternatives.

Setup: create a shared dataset from fixest::base_stagg. Never-treated units use NA for their timing column — this is the convention for all three staggered estimators.

df_stagg <- fixest::base_stagg
df_stagg$timing <- df_stagg$year_treated
df_stagg$timing[df_stagg$year_treated == 10000] <- NA # mark never-treated

Callaway & Sant’Anna (2021) — estimator = "cs"

Estimates a separate ATT(g,t) for every combination of cohort g and calendar time t, then aggregates to the event-study curve. Comparison group can be never-treated units (default) or not-yet-treated units.

cs <- run_es(
  data = df_stagg,
  outcome = y,
  time = year,
  timing = timing, # first treatment period; NA = never treated
  unit = id,
  staggered = TRUE,
  estimator = "cs",
  control_group = "nevertreated" # or "notyettreated"
)

print(cs)

## Event Study Result (fixes)
##   N: 950  | Units: 95  | Treated units: 45  | Never-treated: 50 
##   FE: 
##   VCOV: analytic  | Cluster: - 
##   Method: classic  | lead_range: 9  lag_range: 8  baseline: -1

plot_es(cs)

Visualise the full ATT(g,t) matrix

plot_att_gt() shows every cohort × calendar-time cell, making it easy to spot anticipation effects or heterogeneous dynamics.

plot_att_gt(cs, type = "heatmap")

plot_att_gt(cs, type = "facet")

Sun & Abraham (2021) — estimator = "sa"

Builds cohort × horizon interaction terms, then aggregates with cohort-share weights. Gives the same result as fixest::sunab() but through the unified run_es() interface.

sa <- run_es(
  data = df_stagg,
  outcome = y,
  treatment = treated,
  time = year,
  timing = timing,
  unit = id,
  fe = ~ id + year,
  staggered = TRUE,
  estimator = "sa",
  cluster = ~id
)

print(sa)

## Event Study Result (fixes)
##   N: 950  | Units: 95  | Treated units: 45  | Never-treated: 50 
##   FE: id + year
##   VCOV: HC1  | Cluster: id 
##   Method: classic  | lead_range: 9  lag_range: 8  baseline: -1

plot_es(sa)

Borusyak, Jaravel & Spiess (2024) — estimator = "bjs"

A three-step imputation approach:

1. Fit a TWFE model on untreated observations only (never-treated + not-yet-treated).
2. Impute each treated unit’s counterfactual outcome.
3. Average the imputed treatment effects by event-study horizon.

bjs <- run_es(
  data = df_stagg,
  outcome = y,
  time = year,
  timing = timing,
  unit = id,
  staggered = TRUE,
  estimator = "bjs"
)

print(bjs)

## Event Study Result (fixes)
##   N: 950  | Units: 95  | Treated units: 45  | Never-treated: 50 
##   FE: id + year
##   VCOV: bjs_conservative  | Cluster: - 
##   Method: classic  | lead_range: 1  lag_range: 8  baseline: -1

plot_es(bjs)

Bootstrap simultaneous confidence bands

Pointwise confidence intervals control error rates one period at a time. When you display 15 pre- and post-treatment estimates on a single plot, the chance that at least one interval incorrectly excludes zero can be much higher than the nominal 5%.

Simultaneous bands (Callaway & Sant’Anna 2021, Corollary 1) provide joint coverage: with probability ≥ 1 − α, the entire event-study curve is contained within the band.

Use bootstrap = TRUE in run_es() and show_simultaneous = TRUE in plot_es():

cs_boot <- run_es(
  data = df_stagg,
  outcome = y,
  time = year,
  timing = timing,
  unit = id,
  staggered = TRUE,
  estimator = "cs",
  control_group = "nevertreated",
  bootstrap = TRUE, # run multiplier bootstrap (Algorithm 1, CS 2021)
  B = 999, # number of bootstrap draws; 999 recommended in practice
  boot_seed = 42 # for reproducibility
)

# Lighter outer band = simultaneous CI; darker inner band = pointwise CI
plot_es(cs_boot, show_simultaneous = TRUE)

The simultaneous band is always at least as wide as the pointwise band. The interactive plot also gains a second ribbon trace:

plot_es_interactive(cs_boot, show_simultaneous = TRUE)

Plotting options

plot_es() works with results from any estimator.

# Error bars instead of ribbon
plot_es(es, type = "errorbar")

# Multiple CI levels at once
es_multi <- run_es(
  data = df,
  outcome = y,
  treatment = treat,
  time = period,
  timing = 5,
  fe = ~ id + period,
  cluster = ~id,
  conf.level = c(0.90, 0.95, 0.99)
)
plot_es(es_multi, ci_level = 0.90, theme_style = "minimal")

Interactive plots

plot_es_interactive() produces a plotly chart with hover tooltips (requires the plotly package).

plot_es_interactive(es)

Key run_es() arguments

Contributing

Found a bug or have a feature request? Open an issue on GitHub.