Getting started with nonabsdid

library(nonabsdid)

nonabsdid provides three things on top of the heterogeneity-robust DiD estimators that already exist in R:

A unified front door, nabs_event_study(), that runs DCDH, PanelMatch, or any of the fect family (FE / IFE / MC) with a common argument set.
An S3 generic, as_nabs_event_study(), that converts each estimator’s native output into a tidy tibble with a stable schema.
A plotting function, nabs_event_plot(), that overlays any combination of those tidy objects on a single ggplot2 panel, with an optional naive TWFE reference series.

Plus a one-line shortcut, nabs_event_study_simple(), that runs all three heterogeneity-robust estimators with sensible defaults and gives you a single overlay plot — meant for the very first look at the data.

The estimator packages themselves (DIDmultiplegtDYN, PanelMatch, fect, fixest) are suggested, not required, so install only the ones you plan to use.

The 30-second version

res <- nabs_event_study_simple(sim,
                               outcome = "y", treatment = "d",
                               unit = "id", time = "t")
res$plot

That’s it. Window lengths are auto-picked from the panel. All available heterogeneity-robust estimators run (skipped silently if their package isn’t installed), the naive TWFE reference is overlaid in grey, and you get a single figure to inspect.

For everything below — picking specific estimators, wider control over options, comparing FE vs IFE vs MC — read on.

A simulated panel

We simulate a panel with non-absorbing treatment so that all three estimators have something to chew on. Half of the units turn on at t = 10 and half of those turn back off at t = 16.

set.seed(2026)
N <- 80; TT <- 24
sim <- expand.grid(id = seq_len(N), t = seq_len(TT))

# Treatment switches on at t=10 for ids <= N/2, and off at t=16 for ids <= N/4.
sim$d <- with(sim, as.integer(
  (id <= N/2 & t >= 10 & !(id <= N/4 & t >= 16))
))

# Heterogeneous, time-varying effect: 1 for early, 0.5 for late.
sim$tau <- with(sim, ifelse(id <= N/4, 1.0, 0.5))
sim$y <- with(sim, 0.05 * id + 0.03 * t + d * tau + rnorm(nrow(sim), sd = 0.3))

Three estimators, three lines of code

res_dcdh <- nabs_event_study(sim, outcome = "y", treatment = "d",
                         unit = "id", time = "t",
                         method = "DCDH", lags = 4, leads = 6)

res_pm   <- nabs_event_study(sim, outcome = "y", treatment = "d",
                         unit = "id", time = "t",
                         method = "PanelMatch", lags = 4, leads = 6)

res_ife  <- nabs_event_study(sim, outcome = "y", treatment = "d",
                         unit = "id", time = "t",
                         method = "IFE")

Each nabs_event_study() return is a list with tidy (an nabs_event_study_tbl), fit (the native estimator object, for diagnostics), and call.

Or call the estimators directly

If you want full control of estimator-specific options, call the underlying package and tidy the result:

fit <- DIDmultiplegtDYN::did_multiplegt_dyn(
  df = sim, outcome = "y", group = "id", time = "t",
  treatment = "d", effects = 6, placebo = 4,
  cluster = "id"
)
tidy_dcdh <- as_nabs_event_study(fit, outcome = "y")

For PanelMatch, remember to pass the placebo result via pre_obj:

panel <- PanelMatch::PanelData(sim, "id", "t", "d", "y")
pm    <- PanelMatch::PanelMatch(panel.data = panel, lag = 4, lead = 0:6,
                                refinement.method = "ps.match",
                                size.match = 10, qoi = "att",
                                placebo.test = TRUE,
                                forbid.treatment.reversal = FALSE)
pe    <- PanelMatch::PanelEstimate(pm, panel.data = panel)
pl    <- PanelMatch::placebo_test(pm, panel.data = panel, plot = FALSE)

tidy_pm <- as_nabs_event_study(pe, pre_obj = pl)

Naive TWFE reference

For a visual baseline, fit a naive TWFE event study with fixest:

ref <- naive_twfe(sim, outcome = "y", treatment = "d",
                  unit = "id", time = "t",
                  lags = 4, leads = 6)

The reference is built from treatment changes (a distributed-lag specification), so it handles the on/off treatment simulated above rather than assuming a single absorbing onset. It is still intended only as a reference: it is not robust to treatment-effect heterogeneity – which is the point. Showing it next to the robust estimators makes the correction visible.

Plot

nabs_event_plot(
  res_dcdh$tidy, res_pm$tidy, res_ife$tidy,
  reference = ref,
  xlim = c(-4, 6),
  ylim = c(-1, 2),
  ylab = "Effect on y"
)

The reference series is drawn in grey20 (configurable via reference_color) and dashed, so it sits visually behind the main estimators. Pre-treatment periods get round markers; post-treatment get triangles. Each method gets its own color pair; the default palette is patterned after the conventions in applied DCDH/PanelMatch papers, with red shades pre and blue/green post.

Schema

Every tidier returns a tibble with the same columns:

column	type	description
`time`	int	Relative period (0 = treatment onset).
`estimate`	num	Point estimate.
`std.error`	num	Standard error (may be `NA`).
`conf.low`	num	Lower CI bound.
`conf.high`	num	Upper CI bound.
`window`	chr	`"pre"` if `time < 0`, else `"post"`.
`method`	chr	`"DCDH"`, `"PanelMatch"`, `"IFE"`, `"TWFE"`, or custom.
`outcome`	chr	Outcome variable name.

Anything you can coerce to a data frame with at least time and estimate columns can also flow through as_nabs_event_study(). This makes it easy to plug in additional estimators later – write a one-line method that pulls the right slots, and the plotting code keeps working.