Package {nonabsdid}

Type:	Package
Title:	Visualize Heterogeneity-Robust Event Studies for Non-Absorbing Treatments
Version:	0.3.2
Description:	Runs several heterogeneity-robust difference-in-differences (DID) event-study estimators for non-absorbing (i.e., treatment can switch on and off) binary treatments through their own packages, harmonizes their output onto a common time axis and tidy data structure, and overlays them in a single 'ggplot2' panel for visual comparison. Supported estimators include those provided by 'DIDmultiplegtDYN', 'PanelMatch', and 'fect', with an optional naive two-way fixed-effects reference series via 'fixest'. The underlying methods are respectively described in Clement de Chaisemartin and Xavier D'Haultfoeuille. "Difference-in-Differences Estimators of Intertemporal Treatment Effects." The Review of Economics and Statistics (2026) <doi:10.1162/rest_a_01414>, Kosuke Imai, In Song Kim, and Erik H. Wang. "Matching methods for causal inference with time‐series cross‐sectional data." American Journal of Political Science 67.3 (2023) <doi:10.1111/ajps.12685>, Licheng Liu, Ye Wang, and Yiqing Xu. "A practical guide to counterfactual estimators for causal inference with time‐series cross‐sectional data." American Journal of Political Science 68.1 (2024) <doi:10.1111/ajps.12723>, and Laurent R. Bergé, Kyle Butts, and Grant McDermott. "Fast and user-friendly econometrics estimations: The R package 'fixest'." arXiv preprint (2026) <doi:10.48550/arXiv.2601.21749>. A single nabs_event_study() wrapper runs any supported estimator with a common interface; nabs_event_study_simple() provides a one-line front door for quick exploratory runs; the S3 generic as_nabs_event_study() coerces estimator output into a tidy stable schema; and nabs_event_plot() overlays multiple methods on a single 'ggplot2' panel, with optional naive two-way fixed effects drawn in a neutral color as a reference.
License:	MIT + file LICENSE
Encoding:	UTF-8
Depends:	R (≥ 4.1.0)
Imports:	cli, dplyr, ggplot2, rlang, stats, tibble
Suggests:	DIDmultiplegtDYN, polars, PanelMatch, fect, fixest, knitr, rmarkdown, testthat (≥ 3.0.0), vdiffr, withr
Config/testthat/edition:	3
URL:	https://github.com/takuma1102/nonabsdid
BugReports:	https://github.com/takuma1102/nonabsdid/issues
VignetteBuilder:	knitr
Additional_repositories:	https://rpolars.r-universe.dev
Config/roxygen2/version:	8.0.0
NeedsCompilation:	no
Packaged:	2026-06-10 23:43:04 UTC; 81809
Author:	Takuma Iwasaki [aut, cre]
Maintainer:	Takuma Iwasaki <iwasakit@stanford.edu>
Repository:	CRAN
Date/Publication:	2026-06-18 13:50:02 UTC

nonabsdid: Side-by-Side Event-Study Comparison for Heterogeneous DiD

Description

The 'nonabsdid' package provides a single, consistent interface for running, tidying, and plotting event-study estimates from several heterogeneity-robust difference-in-differences estimators that support non-absorbing (switching on/off) treatments:

Details

• **DCDH** — de Chaisemartin & D'Haultfoeuille, via 'DIDmultiplegtDYN::did_multiplegt_dyn()'.

• **PanelMatch** — Imai, Kim & Wang, via 'PanelMatch::PanelMatch()' / 'PanelMatch::PanelEstimate()' with pre-treatment results from 'PanelMatch::placebo_test()'.

• **IFE / Imputation** — Liu, Wang & Xu, via 'fect::fect()'.

The user-facing API has three pieces:

• [nabs_event_study()] runs one of the estimators with a unified argument set and returns its native object plus a tidy tibble.

• [as_nabs_event_study()] is an S3 generic that coerces native estimator objects into a stable tidy tibble (the *nabs_event_study_tbl* schema).

• [nabs_event_plot()] takes one or more *nabs_event_study_tbl* objects and overlays them on a single ggplot2 panel, optionally with a naive two-way fixed effects (TWFE) reference series in a neutral color.

Tidy schema

All tidiers return a tibble with class 'c("nabs_event_study_tbl", "tbl_df", ...)' and the following columns:

'time'

Integer relative period (0 = treatment onset).

'estimate'

Point estimate.

'std.error'

Standard error (may be 'NA' when the estimator only reports CI bounds, e.g. some 'fect' configurations).

'conf.low', 'conf.high'

Lower / upper bound of the 'conf.level' confidence interval.

'window'

'"pre"' if 'time < 0', otherwise '"post"'.

'method'

Method label, e.g. '"DCDH"', '"PanelMatch"', '"IFE"', or '"TWFE"'.

'outcome'

Outcome variable name (when known), else 'NA'.

Author(s)

Maintainer: Takuma Iwasaki iwasakit@stanford.edu (ORCID)

Authors:

• Takuma Iwasaki iwasakit@stanford.edu (ORCID)

See Also

Useful links:

• https://github.com/takuma1102/nonabsdid

• Report bugs at https://github.com/takuma1102/nonabsdid/issues

Coerce an estimator result to a tidy event-study tibble

Description

'as_nabs_event_study()' is an S3 generic that converts the native output object of a supported estimator into the unified *nabs_event_study_tbl* schema used by [nabs_event_plot()]. Methods exist for objects of class '"did_multiplegt_dyn"' (from 'DIDmultiplegtDYN'), '"PanelEstimate"' (from 'PanelMatch'), '"fect"' (from 'fect'), and '"fixest"' (from 'fixest', used for the naive TWFE reference series).

Usage

as_nabs_event_study(
  x,
  method = NULL,
  outcome = NA_character_,
  conf.level = 0.95,
  ...
)

## S3 method for class 'fixest'
as_nabs_event_study(
  x,
  method = NULL,
  outcome = NA_character_,
  conf.level = 0.95,
  ...
)

## S3 method for class 'did_multiplegt_dyn'
as_nabs_event_study(
  x,
  method = NULL,
  outcome = NA_character_,
  conf.level = 0.95,
  ...
)

## S3 method for class 'fect'
as_nabs_event_study(
  x,
  method = NULL,
  outcome = NA_character_,
  conf.level = 0.95,
  ...
)

## S3 method for class 'list'
as_nabs_event_study(
  x,
  method = NULL,
  outcome = NA_character_,
  conf.level = 0.95,
  ...
)

## S3 method for class 'nabs_event_study_result'
as_nabs_event_study(
  x,
  method = NULL,
  outcome = NA_character_,
  conf.level = 0.95,
  ...
)

## S3 method for class 'nabs_event_study_simple'
as_nabs_event_study(
  x,
  method = NULL,
  outcome = NA_character_,
  conf.level = 0.95,
  ...
)

## S3 method for class 'PanelEstimate'
as_nabs_event_study(
  x,
  method = NULL,
  outcome = NA_character_,
  conf.level = 0.95,
  pre_obj = NULL,
  add_reference = TRUE,
  ...
)

Arguments

Details

A 'data.frame' method is also provided as an escape hatch: it accepts any frame that already contains 'time' and 'estimate' columns and fills in the rest of the schema if missing.

## fixest method

Extracts coefficients on 'time_to_event' interactions of the form 'time_to_event::<k>' or 'time_to_event::<k>:<interaction>', the coefficient names produced by 'fixest::i()'. These are treated as event-study *levels* (the classic absorbing-treatment parametrisation). Standard errors come from the model's clustered VCOV; confidence intervals use the normal approximation and 'conf.level'.

Note that [naive_twfe()] does not fit this absorbing parametrisation itself – it uses a distributed-lag design in treatment levels – but this method is retained so that models you fit yourself with 'fixest::i()' can still be tidied.

## fect method

'fect::fect()' returns event-study coordinates in '$time' and '$att', with confidence-interval bounds in the two-column matrix '$att.bound'. Standard errors are pulled from '$est.att[, "S.E."]' when available; if the object was fit without 'se = TRUE', only the point estimates are returned and SE / CI columns are filled with 'NA'.

The 'method' label is auto-detected from 'x$method', the option that was passed to 'fect::fect()':

• '"fe"' -> '"FE"' (two-way fixed-effects imputation; Borusyak-style)

• '"ife"' -> '"IFE"' (interactive fixed effects; Bai 2009)

• '"mc"' -> '"MC"' (matrix completion; Athey et al. 2021)

Pass an explicit 'method' argument to override this auto-detected label.

## PanelMatch method

For 'PanelMatch::PanelEstimate()' the post-treatment leads are stored as '$estimate' / '$standard.error' (singular). The pre-treatment placebo results from 'PanelMatch::placebo_test()' use '$estimates' / '$standard.errors' (plural). To produce a single event-study path, pass the placebo object via 'pre_obj':

  pm <- PanelMatch::PanelMatch(...)
  pe <- PanelMatch::PanelEstimate(pm, panel.data = pd)
  pl <- PanelMatch::placebo_test(pm, panel.data = pd, plot = FALSE)
  tidy <- as_nabs_event_study(pe, pre_obj = pl)

A 'time = -1' reference point with 'estimate = 0' is inserted so that the event-study path is anchored at t = -1, matching common practice and the 'did' / 'fixest::iplot' convention. Disable with 'add_reference = FALSE'.

Value

A tibble of class '"nabs_event_study_tbl"' with one row per relative period and the columns documented in the package overview.

Examples

# The data.frame escape hatch needs no estimator packages: pass a frame
# that already has `time` and `estimate`; the remaining schema columns
# (including CIs derived from `std.error`) are filled in automatically.
raw <- data.frame(
  time      = -3:4,
  estimate  = c(-0.05, 0.01, 0.00, 0.02, 0.30, 0.42, 0.38, 0.50),
  std.error = 0.12
)
tidy_fit <- as_nabs_event_study(raw, method = "DCDH", outcome = "y")

# With the DCDH estimator installed, coerce its native object directly.
if (requireNamespace("DIDmultiplegtDYN", quietly = TRUE) &&
    requireNamespace("polars", quietly = TRUE)) {
  set.seed(1)
  library(polars)
  panel <- expand.grid(id = 1:60, t = 1:10)
  panel$d <- with(panel, as.integer(
    (id %% 4 == 1 & t %in% 4:7) |
    (id %% 4 == 2 & t %in% 5:8) |
    (id %% 4 == 3 & t %in% 6:9)
  ))
  panel$y <- 0.2 * panel$t + 0.5 * panel$d + rnorm(nrow(panel))

  fit <- DIDmultiplegtDYN::did_multiplegt_dyn(
    df = panel,
    outcome = "y",
    group = "id",
    time = "t",
    treatment = "d",
    effects = 3,
    placebo = 2
  )
  as_nabs_event_study(fit, outcome = "y")
}

Plot one or more event-study tibbles on a single panel

Description

Overlays event-study estimates from any combination of supported estimators on a single ggplot2 panel. Two visual encodings are available via 'style':

Usage

nabs_event_plot(
  ...,
  style = c("prepost_color", "method_shape"),
  connect = FALSE,
  connect_linewidth = 0.4,
  reference = NULL,
  reference_color = "grey20",
  palette = "default",
  shapes = NULL,
  xlim = NULL,
  ylim = NULL,
  dodge = 0.5,
  point_size = 2.5,
  errorbar_width = 0.1,
  x_break_by = 2,
  show_pre_post_legend = TRUE,
  xlab = "Relative time to treatment change",
  ylab = "Estimated effect",
  base_size = 11
)

Arguments

Details

* '"prepost_color"' (default) – each method gets its own color, with separate shades for pre- and post-treatment periods, mirroring common conventions in DCDH-style plots. Points are drawn as circles throughout. * '"method_shape"' – each method gets a single color *and* a single marker shape. Pre and post periods share both the color and the shape; they are told apart only by their position relative to time 0. Because method is double-encoded (color + shape), this style stays legible in grayscale.

An optional 'reference' series – typically a naive TWFE fit from [naive_twfe()] – is drawn in a neutral color (default black) so the reader can see what the heterogeneity-robust estimators are correcting against.

Set ‘connect = TRUE' to join each series’ point estimates with a thin line, in addition to the points and error bars.

Value

A 'ggplot' object.

Examples

dcdh_tidy <- as_nabs_event_study(
  data.frame(
    time = -2:3,
    estimate = c(-0.06, -0.02, 0.10, 0.22, 0.28, 0.31),
    std.error = 0.08
  ),
  method = "DCDH",
  outcome = "y"
)

ife_tidy <- as_nabs_event_study(
  data.frame(
    time = -2:3,
    estimate = c(-0.04, 0.00, 0.08, 0.18, 0.25, 0.27),
    std.error = 0.10
  ),
  method = "IFE",
  outcome = "y"
)

nabs_event_plot(dcdh_tidy, ife_tidy, xlim = c(-2, 3))
nabs_event_plot(dcdh_tidy, ife_tidy, style = "method_shape", connect = TRUE)

Run an event-study estimator with a unified interface

Description

'nabs_event_study()' is a thin wrapper around the three supported estimators (DCDH, PanelMatch, IFE/fect) that takes a single, common argument set and dispatches to the correct underlying package. It is **not** intended to expose every option of every estimator; for that, call the underlying packages directly and tidy their output with [as_nabs_event_study()].

Usage

nabs_event_study(
  data,
  outcome,
  treatment,
  unit,
  time,
  method = c("DCDH", "PanelMatch", "IFE", "FE", "MC"),
  lags = 6L,
  leads = 8L,
  controls = NULL,
  cluster = unit,
  conf.level = 0.95,
  ...
)

Arguments

Details

What it does cover:

• Variable names (outcome, treatment, unit, time),

• Pre/post window length ('lags', 'leads'),

• Optional covariates and clustering,

• Reasonable defaults that match the three packages' typical use.

Value

A list of class '"nabs_event_study_result"' with elements:

'tidy'

An 'nabs_event_study_tbl'.

'fit'

The native estimator object (for diagnostics).

'call'

The call that produced it.

Examples

 if (requireNamespace("DIDmultiplegtDYN", quietly = TRUE) &&
     requireNamespace("polars", quietly = TRUE)) {
  set.seed(1)
  library(polars)
  panel <- expand.grid(id = 1:60, t = 1:10)
  panel$d <- with(panel, as.integer(
    (id %% 4 == 1 & t %in% 4:7) |
    (id %% 4 == 2 & t %in% 5:8) |
    (id %% 4 == 3 & t %in% 6:9)
  ))
  panel$y <- 0.2 * panel$t + 0.5 * panel$d + rnorm(nrow(panel))

  res_dcdh <- nabs_event_study(
    panel,
    outcome = "y",
    treatment = "d",
    unit = "id",
    time = "t",
    method = "DCDH",
    lags = 2,
    leads = 2
  )
  res_dcdh$tidy
}

One-line exploratory front door for non-absorbing event studies

Description

'nabs_event_study_simple()' is a deliberately opinionated convenience wrapper for the *first 30 seconds* of an analysis. You give it your data and the four column names that identify outcome / treatment / unit / time, and it tries to give you a sensible event-study figure with as little typing as possible.

Usage

nabs_event_study_simple(
  data,
  outcome,
  treatment,
  unit,
  time,
  methods = c("DCDH", "PanelMatch", "IFE"),
  include_twfe = TRUE,
  lags = NULL,
  leads = NULL,
  controls = NULL,
  verbose = TRUE,
  ...
)

Arguments

Details

By default it runs **all three** heterogeneity-robust estimators (DCDH, PanelMatch, IFE) plus a naive TWFE reference, and returns a single overlay plot along with the tidy tibbles and raw fits. Use it to *see the picture quickly*; for a careful, publication-ready result, switch to [nabs_event_study()] and tune options per estimator.

If a particular estimator's package is not installed, that estimator is silently skipped with a message and the rest are still attempted. This is intentional: the goal of '_simple()' is to give you *something* to look at even if your environment isn't fully provisioned.

Errors from a single estimator (for instance, PanelMatch failing because there are too few clean controls in the lag window) are caught, reported as a warning, and the remaining estimators continue.

Value

A list of class '"nabs_event_study_simple"' with elements:

'plot'

A 'ggplot' object; the overlay figure.

'tidy'

A single combined 'nabs_event_study_tbl' with all methods.

'per_method'

Named list of per-method tidy tibbles.

'fits'

Named list of native estimator objects.

'twfe'

The TWFE reference (or 'NULL').

'call'

The matched call.

Examples

 if (requireNamespace("DIDmultiplegtDYN", quietly = TRUE) &&
     requireNamespace("polars", quietly = TRUE)) {
  set.seed(1)
  library(polars)
  panel <- expand.grid(id = 1:60, t = 1:10)
  panel$d <- with(panel, as.integer(
    (id %% 4 == 1 & t %in% 4:7) |
    (id %% 4 == 2 & t %in% 5:8) |
    (id %% 4 == 3 & t %in% 6:9)
  ))
  panel$y <- 0.2 * panel$t + 0.5 * panel$d + rnorm(nrow(panel))

  res <- nabs_event_study_simple(
    panel,
    outcome = "y",
    treatment = "d",
    unit = "id",
    time = "t",
    methods = "DCDH",
    include_twfe = FALSE,
    lags = 2,
    leads = 2,
    verbose = FALSE
  )
  res$tidy
}

Estimate a naive two-way fixed-effects (TWFE) event study

Description

Runs a basic event-study TWFE regression of 'outcome' on leads and lags of the treatment, with unit and time fixed effects, using 'fixest::feols()'. The result is **deliberately unsophisticated** – the point of 'nonabsdid' is to contrast this naive benchmark against heterogeneity-robust estimators (DCDH, 'fect', PanelMatch).

Usage

naive_twfe(
  data,
  outcome,
  treatment,
  unit,
  time,
  lags = 12L,
  leads = 6L,
  controls = NULL,
  cluster = unit,
  conf.level = 0.95
)

Arguments

Details

Unlike a classic event study, 'naive_twfe()' does **not** assume the treatment is absorbing. It is built for binary treatments that can switch on *and off* over time (e.g. a policy that is repealed, a subsidy that lapses). It fits a distributed-lag TWFE in the treatment *levels*,

y_{it} = \alpha_i + \gamma_t + \sum_{k} \beta_k D_{i,t+k} + \varepsilon_{it},

i.e. the outcome on the leads and lags of the treatment indicator with unit and time fixed effects. The coefficient on lag 'k' is reported at event time '+k' and the coefficient on lead 'k' at event time '-k', so the path is defined relative to a treatment *change* rather than to a single absorbing onset. Event time '-1' is the omitted reference. Each \beta_k is a partial correlation, not a heterogeneity-robust dynamic effect – that is the point of the benchmark.

The naming of 'lags'/'leads' follows the package convention used elsewhere (and in the README): 'lags' counts pre-periods, 'leads' counts post-periods, so 'lags = 6, leads = 8' yields event times on '[-6, 8]'.

Coefficients and standard errors are read directly from the fitted model (clustered as requested); the reference period '-1' is reported as exactly zero.

Value

An 'nabs_event_study_tbl' with 'method = "TWFE"'. The fitted 'fixest' model is attached as the '"fit"' attribute.

Examples

df <- data.frame(
  id = rep(1:4, each = 8),
  yr = rep(1:8, times = 4),
  d  = c(rep(0, 8),
         0, 0, 1, 1, 1, 0, 0, 0,
         0, 0, 0, 1, 1, 1, 1, 0,
         rep(0, 8)),
  y  = rnorm(32)
)
naive_twfe(df, outcome = "y", treatment = "d",
           unit = "id", time = "yr", lags = 2, leads = 3)