Compare TROP standard-error methods on one fit

Refits the TROP estimator under several inference methods — the unit-level block bootstrap and/or the delete-one-unit jackknife (over every unit) — and returns the estimate, standard error and confidence interval from each on the common cf_att_tbl schema. The penalties are chosen once (a single se = "none" fit — by cross-validation, or fixed up front with lambda) and reused for every inference method, exactly as trop_ablation() reuses one fit's penalties, so the ATT is identical across rows and only the uncertainty changes. This is the table to reach for when checking how sensitive the confidence interval is to the choice of resampling scheme — it is an inference-mode comparison, not an estimator comparison, so all rows share one point estimate by construction.

Usage

compare_se_modes(
  data,
  outcome,
  treatment,
  unit,
  time,
  covariates = NULL,
  lambda = NULL,
  se = c("bootstrap", "jackknife"),
  anchor = "pooled",
  control = trop_control(),
  labels = NULL
)

Arguments

data, outcome, treatment, unit, time, covariates

Passed to trop().

lambda

Optional named list list(time=, unit=, nn=) to fix the penalties and skip cross-validation, exactly as in trop(). When NULL (the default) the penalties are chosen once by cross-validation; either way the resolved penalties are shared by every inference method.

se

Character vector of inference methods to compare; any of "bootstrap", "jackknife". Defaults to both. (To compare anchors or penalty constraints instead, see trop()'s anchor argument and trop_ablation().)

anchor

Weight anchoring, as in trop() (default "pooled").

control

A trop_control() list, shared by every fit. Set n_boot here to control the bootstrap replications.

labels

Optional method labels, one per se; defaults to "Bootstrap SE" / "Jackknife SE".

Value

An object of class cf_se_comparison (extending cf_att_tbl) with one row per inference method, sharing a common point estimate. If a method does not apply to the design (for example the bootstrap with a single control unit) it still contributes a row, carrying the shared estimate with NA uncertainty and an explanatory note.

See also

trop(), trop_ablation(), bind_att()

Examples

df <- sim_panel(N = 16, T = 9, n_treated = 3, t0 = 6, att = 2, seed = 1)
# \donttest{
compare_se_modes(df, "y", "w", "id", "t",
                 control = trop_control(n_cv_cells = 5L, cv_cycles = 1L,
                                        tol = 1e-3, n_boot = 20L,
                                        seed = 1L))
#>        method estimate std.error conf.low conf.high n_treated_cells
#>  Bootstrap SE   2.5253    0.2278   2.0600    2.8166              12
#>  Jackknife SE   2.5253    0.2901   1.9567    3.0940              12
#>  n_treated_units outcome    engine rank note
#>                3       y cfcompare    1 <NA>
#>                3       y cfcompare    1 <NA>

# Fix the penalties (skip cross-validation), as with trop()'s `lambda`:
compare_se_modes(df, "y", "w", "id", "t",
                 lambda = list(time = 0, unit = 0, nn = Inf),
                 control = trop_control(n_boot = 20L))
#>        method estimate std.error conf.low conf.high n_treated_cells
#>  Bootstrap SE   2.5252    0.2263   2.1471    2.9478              12
#>  Jackknife SE   2.5252    0.2705   1.9951    3.0553              12
#>  n_treated_units outcome    engine rank note
#>                3       y cfcompare    0 <NA>
#>                3       y cfcompare    0 <NA>
# }