Modern Difference-in-Differences Estimators

Introduction

Traditional two-way fixed effects (TWFE) estimation of event studies can produce biased estimates when treatment effects are heterogeneous across cohorts and time. The EventStudy package supports five panel event study estimators:

Method	Package	Key Property
TWFE	Base R	Standard two-way fixed effects
Sun-Abraham	`fixest`	Interaction-weighted estimator
Callaway-Sant’Anna	`did`	Group-time ATTs with doubly-robust estimation
de Chaisemartin-D’Haultfoeuille	`DIDmultiplegt`	Fuzzy DiD, robust to heterogeneous effects
Borusyak-Jaravel-Spiess	`didimputation`	Imputation-based estimator

Data Setup

All estimators use the PanelEventStudyTask:

library(EventStudy)
library(tibble)

# Create panel data
panel_data <- tibble(
  unit_id = rep(1:100, each = 20),
  time_id = rep(1:20, times = 100),
  treatment = ifelse(unit_id <= 50 & time_id >= 11, 1, 0),
  outcome = rnorm(2000) + 2 * treatment  # true effect = 2
)

task <- PanelEventStudyTask$new(
  data = panel_data,
  unit_col = "unit_id",
  time_col = "time_id",
  treatment_col = "treatment",
  outcome_col = "outcome"
)

TWFE (Default)

result <- estimate_panel_event_study(task, method = "twfe", leads = 5, lags = 5)
plot_panel_event_study(result)

Sun-Abraham

The interaction-weighted estimator of Sun and Abraham (2021) avoids negative weighting problems in TWFE by computing cohort-specific estimates. Requires fixest.

result <- estimate_panel_event_study(
  task,
  method = "sun_abraham",
  leads = 5,
  lags = 5
)
plot_panel_event_study(result)

Callaway-Sant’Anna

The doubly-robust estimator of Callaway and Sant’Anna (2021) estimates group-time average treatment effects and aggregates them into dynamic effects. Requires did.

result <- estimate_panel_event_study(
  task,
  method = "callaway_santanna",
  leads = 5,
  lags = 5
)
plot_panel_event_study(result)

Key features: - Doubly-robust (consistent if either outcome or propensity score model is correct) - Handles staggered treatment adoption - Provides group-time specific estimates

de Chaisemartin-D’Haultfoeuille

The estimator of de Chaisemartin and D’Haultfoeuille (2020) is robust to heterogeneous treatment effects and can handle fuzzy designs. Requires DIDmultiplegt.

result <- estimate_panel_event_study(
  task,
  method = "dechaisemartin_dhaultfoeuille",
  leads = 5,
  lags = 5
)
plot_panel_event_study(result)

Borusyak-Jaravel-Spiess

The imputation estimator of Borusyak, Jaravel, and Spiess (2024) imputes untreated potential outcomes and computes treatment effects as the difference. Requires didimputation.

result <- estimate_panel_event_study(
  task,
  method = "borusyak_jaravel_spiess",
  leads = 5,
  lags = 5
)
plot_panel_event_study(result)

Key features: - Efficient imputation-based approach - Handles staggered treatment timing - Provides horizon-specific estimates

Comparing Estimators

All estimators produce the same output format (relative_time, estimate, std.error, statistic, p.value), making it easy to compare:

methods <- c("twfe", "sun_abraham", "callaway_santanna")

results <- lapply(methods, function(m) {
  tryCatch(
    estimate_panel_event_study(task, method = m, leads = 5, lags = 5),
    error = function(e) NULL
  )
})
names(results) <- methods

When to Use Which?

TWFE: When treatment is not staggered and effects are homogeneous
Sun-Abraham: Good default for staggered adoption; requires fixest (fast)
Callaway-Sant’Anna: When you want doubly-robust estimation; slower but more robust
de Chaisemartin-D’Haultfoeuille: Fuzzy designs or when treatment can switch on and off
Borusyak-Jaravel-Spiess: Efficient imputation approach for staggered designs

References

Sun, L. and Abraham, S. (2021). Estimating dynamic treatment effects in event studies with heterogeneous treatment effects. Journal of Econometrics, 225(2), 175-199.
Callaway, B. and Sant’Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230.
de Chaisemartin, C. and D’Haultfoeuille, X. (2020). Two-Way Fixed Effects Estimators with Heterogeneous Treatment Effects. American Economic Review, 110(9), 2964-2996.
Borusyak, K., Jaravel, X., and Spiess, J. (2024). Revisiting Event Study Designs: Robust and Efficient Estimation. Review of Economic Studies, 91(6), 3253-3285.

Simon Mueller

2026-09-06