ATT aggregation for staggered adoption designs

Estimates average treatment effects on the treated (ATT) using either the Callaway-Sant'Anna (2021) or Borusyak-Jaravel-Spiess (2024) estimator, aggregated to a single summary effect ("simple"), per-cohort effects ("by_cohort"), or per calendar-time effects ("by_time").

Usage

att(
  data,
  outcome,
  time,
  timing,
  unit,
  estimator = c("cs", "bjs"),
  aggregation = c("simple", "by_cohort", "by_time"),
  control_group = c("nevertreated", "notyettreated"),
  anticipation = 0L,
  conf_level = 0.95,
  interval = 1,
  time_transform = FALSE
)

Arguments

data: A data.frame containing panel data.
outcome: Unquoted outcome variable (name or expression, e.g., log(y)).
time: Unquoted calendar time variable (numeric).
timing: Unquoted column giving each unit's first treatment period (NA = never treated).
unit: Unquoted unit identifier (required).
estimator: Estimation strategy: "cs" (Callaway-Sant'Anna 2021, default) or "bjs" (Borusyak-Jaravel-Spiess 2024).
aggregation: Aggregation type: "simple" (overall ATT, default), "by_cohort" (one ATT per treatment cohort), or "by_time" (one ATT per calendar time period).
control_group: For estimator = "cs": comparison group, "nevertreated" (default) or "notyettreated".
anticipation: For estimator = "cs": number of anticipation periods before treatment (non-negative integer, default 0L).
conf_level: Numeric confidence level(s) (default 0.95). Multiple levels are supported, e.g., c(0.90, 0.95).
interval: Numeric time spacing (default 1; informational only).
time_transform: Logical; if TRUE, creates consecutive integer time within unit via dplyr::dense_rank(). Requires unit.

Value

A data.frame of class "att_result" with columns:

group: Cohort or calendar time (NA for "simple").
estimate: ATT point estimate.
std.error: Standard error.
statistic: t-statistic (estimate / std.error).
p.value: Two-sided p-value (normal approximation).
conf_low_XX, conf_high_XX: CI bounds for each conf.level.

Attributes: aggregation, estimator, conf.level, N, N_units, N_treated, N_nevertreated, control_group (CS only), att_gt (CS raw ATT(g,t) table), tau_it (BJS unit-time effects table).

Details

This function complements event_study(): use event_study() when you want a full event-study curve (dynamic effects by relative time), and att() when you want aggregated ATT estimates that collapse the time dimension.

The result has broom::tidy() and broom::glance() methods, so it can be passed to modelsummary::modelsummary() (rendered with tinytable) to build a publication table — including stacking several estimators (e.g. did() TWFE, CS, and BJS) side by side in one table.

att() supersedes calc_att() as of fixes 1.0.0.

Aggregation formulas (CS estimator)

simple: \(\theta = \sum_g (n_g/n_{treated}) \cdot \overline{ATT(g,\cdot)}\) where \(\overline{ATT(g,\cdot)}\) is the mean over post-treatment periods.
by_cohort: \(\theta(g) = \overline{ATT(g,\cdot)}\) per cohort.
by_time: \(\theta(t) = \sum_{g \le t} w(g,t) \cdot ATT(g,t)\) with \(w(g,t) = n_g / \sum_{g' \le t} n_{g'}\).

Standard errors (BJS estimator)

BJS SEs are approximate (naive sample variance of unit-time effects). Cluster-robust SEs for BJS aggregations are planned for a future release.

Examples

if (FALSE) { # \dontrun{
att(df, outcome = y, time = year, timing = g, unit = id)
att(df, outcome = y, time = year, timing = g, unit = id,
    estimator = "bjs", aggregation = "by_cohort")
} # }