Penalty ablation for the TROP estimator

Refits TROP under a sequence of penalty constraints to show how the ATT moves as the estimator is stripped back towards matrix completion and difference-in-differences. This is the robustness exercise behind Table 5 of Athey, Imbens, Qu & Viviano (2026): constraining lambda_unit and/or lambda_time to zero (dropping unit/time weights) and/or lambda_nn to infinity (dropping the low-rank regression adjustment).

Usage

trop_ablation(
  data,
  outcome,
  treatment,
  unit,
  time,
  covariates = NULL,
  control = trop_control(),
  anchor = "pooled",
  se = "none",
  lambda_full = NULL,
  rmse = FALSE,
  horizon = NULL,
  n_pseudo = NULL,
  n_runs = 200L
)

# S3 method for class 'trop_ablation'
print(x, digits = 3, ...)

# S3 method for class 'trop_ablation'
plot(x, file = NULL, width = NULL, height = NULL, res = 200, digits = 3, ...)

Arguments

data, outcome, treatment, unit, time, covariates: Passed to trop().
control: A trop_control() list (used for every fit).
anchor: Weight anchoring, as in trop() (default "pooled").
se: Standard-error method for every specification (default "none"; any trop() method is allowed, e.g. "jackknife"). When not "none", the standard error and confidence interval appear in the table.
lambda_full: Optional named list list(time=, unit=, nn=) to fix the full specification's penalties and skip cross-validation.
rmse: Logical; if TRUE, also score each specification by a placebo RMSE. A pseudo block-treatment (true effect zero) is assigned to control units, every specification is refit at its penalties, and the RMSE is sqrt(mean(estimate^2)) over n_runs draws. The same draws are scored under every specification, so the relative RMSE with TROP (full) = 1 is the quantity reported in Table 5 of the paper. Adds rmse and rmse_rel columns, and makes plot() show the (relative) RMSE instead of the ATT.
horizon, n_pseudo: Placebo design used when rmse = TRUE: the number of final periods to pseudo-treat and the number of pseudo-treated control units. Default to the real design's post length and treated-unit count.
n_runs: Number of placebo draws when rmse = TRUE.
x: A trop_ablation object.
digits: Number of decimal places for the penalties and estimates.
...: Unused.
file: Optional output path. When supplied, the table is rendered to that file instead of the active graphics device; the format is taken from the extension (.png, the default, or .pdf for vector output). For LaTeX or Markdown source use format() instead.
width, height: Figure size in inches. Defaults adapt to the number of rows and columns.
res: Resolution in PPI for the .png device (ignored for .pdf).

Value

An object of class trop_ablation (a data.frame) with one row per specification: spec, the three penalties lambda_time/lambda_unit/ lambda_nn, estimate, std.error, conf.low/conf.high, and rank. When rmse = TRUE it also has rmse and rmse_rel (RMSE relative to the full TROP).

Details

The full specification's penalties are taken from a single cross-validated trop() fit; every constrained specification then reuses those values where they are not constrained, so the rows are directly comparable. The result is a table object: print it for a clean console table, or format() it to paste-ready LaTeX (booktabs) or Markdown. It is also an ordinary data.frame, so it can be passed to knitr::kable(), gt::gt(), etc.

References

Athey, S., Imbens, G. W., Qu, Z., & Viviano, D. (2026). Triply Robust Panel Estimators. Journal of Applied Econometrics. doi:10.1002/jae.70061 .

Examples

df <- sim_panel(N = 14, T = 8, n_treated = 3, t0 = 5, att = 2, seed = 1)
# fixed base penalties keep the example fast; omit `lambda_full` to select
# them by cross-validation
ab <- trop_ablation(df, "y", "w", "id", "t",
                    lambda_full = list(time = 0.1, unit = 0.5, nn = 2))
ab                                   # clean console table
#> TROP penalty ablation  (outcome: y; anchor: pooled; SE: none)
#> 
#>  Specification             lambda_time lambda_unit lambda_nn ATT   rank(L)
#>  TROP (full)               0.100       0.500       2.000     1.766 3      
#>  No regression adjustment  0.100       0.500       Inf       1.702 0      
#>  No unit weights           0.100       0.000       2.000     1.833 4      
#>  No time weights           0.000       0.500       2.000     1.808 4      
#>  Matrix completion         0.000       0.000       2.000     1.867 5      
#>  Difference-in-differences 0.000       0.000       Inf       1.815 0      
# \donttest{
cat(format(ab, "latex"), sep = "\n") # paste-ready LaTeX (booktabs)
#> \begin{table}[!ht]
#> \centering
#> \caption{TROP penalty ablation}
#> \label{tab:trop-ablation}
#> \begin{tabular}{lrrrrr}
#> \toprule
#> Specification & $\lambda_{\mathrm{time}}$ & $\lambda_{\mathrm{unit}}$ & $\lambda_{\mathrm{nn}}$ & ATT & rank($L$) \\
#> \midrule
#> TROP (full) & 0.100 & 0.500 & 2.000 & 1.766 & 3 \\
#> No regression adjustment & 0.100 & 0.500 & $\infty$ & 1.702 & 0 \\
#> No unit weights & 0.100 & 0.000 & 2.000 & 1.833 & 4 \\
#> No time weights & 0.000 & 0.500 & 2.000 & 1.808 & 4 \\
#> Matrix completion & 0.000 & 0.000 & 2.000 & 1.867 & 5 \\
#> Difference-in-differences & 0.000 & 0.000 & $\infty$ & 1.815 & 0 \\
#> \bottomrule
#> \end{tabular}
#> \end{table}
# }