Package 'cfcompare'

Coerce estimator output to the cfcompare ATT schema

Description

Brings the output of trop(), panel_compare(), a synthdid estimate, or any object with an estimate into the common cf_att_tbl schema, so results computed elsewhere can be slotted into the same comparison and plots.

Usage

as_att(x, ...)

Arguments

x	An object to tidy.
...	Passed to methods (e.g. method, outcome, conf_level).

Value

A cf_att_tbl (a data.frame).

---

Forest plot of ATT estimates across methods

Description

Point estimate and (where available) confidence interval for each method, on a single axis – the quick visual comparison this package is built for.

Usage

## S3 method for class 'cf_comparison'
autoplot(object, ...)

## S3 method for class 'cf_att_tbl'
autoplot(object, ...)

Arguments

object	A cf_comparison (from panel_compare()) or a cf_att_tbl.
...	Unused.

Value

A ggplot2 object.

Examples

df <- sim_panel(seed = 1)
cmp <- panel_compare(df, "y", "w", "id", "t",
                     methods = c("DID", "MC", "TROP"))
autoplot(cmp)

---

Line plot of estimation RMSE versus a design dimension

Description

One line per estimator; the y axis is on a log scale (as in the paper). Lower is better.

Usage

## S3 method for class 'cf_rmse_curve'
autoplot(object, log_y = TRUE, ...)

Arguments

object	A cf_rmse_curve from rmse_curve().
log_y	Logical; log10 y axis (default TRUE).
...	Unused.

Value

A ggplot object.

---

Autoplot for paired RMSE curves

Description

Autoplot for paired RMSE curves

Usage

## S3 method for class 'cf_rmse_curves'
autoplot(object, combined = FALSE, log_y = TRUE, ...)

Arguments

object	A cf_rmse_curves.
combined	FALSE (default) returns a named list of two ggplots; TRUE returns a single side-by-side ggplot (needs the patchwork package).
log_y	Logical; log10 y axis.
...	Unused.

Value

A list of two ggplots, or one combined ggplot.

---

Bar chart of out-of-sample RMSE across methods

Description

Visualises a panel_rmse() result as a ranked bar chart (lowest RMSE first), with +/- 1 standard-error whiskers across placebo runs. This is the cross-model RMSE comparison from the paper.

Usage

## S3 method for class 'cf_rmse_tbl'
autoplot(object, ...)

Arguments

object	A cf_rmse_tbl from panel_rmse().
...	Unused.

Value

A ggplot object.

---

Heatmap of the TROP penalty-sensitivity grid

Description

Cells are coloured by cross-validation loss (darker = better out-of-sample fit) and annotated with the ATT estimate at that penalty pair; the CV-selected cell is outlined.

Usage

## S3 method for class 'cf_trop_grid'
autoplot(object, ...)

Arguments

object	A cf_trop_grid from trop_sensitivity().
...	Unused.

Value

A ggplot object.

---

Synthetic-control-style trajectory plot for a single TROP fit

Description

Draws the treated-unit average observed path against the estimated untreated (counterfactual) path, in the style of the synthdid plot: a dotted line marks the first treated period, the post-treatment gap between the two lines is the estimated effect, and – since TROP carries explicit time weights – the time weights $\theta_s$ are drawn as a ribbon along the bottom to show which periods the counterfactual leans on.

Usage

## S3 method for class 'trop'
autoplot(object, show_weights = TRUE, ...)

Arguments

object	A trop fit from trop().
show_weights	Logical; draw the time-weight ribbon along the bottom.
...	Unused.

Value

A ggplot object.

Examples

df <- sim_panel(N = 20, T = 12, n_treated = 4, t0 = 9, att = 3, seed = 1)
autoplot(trop(df, "y", "w", "id", "t",
              control = trop_control(n_cv_cells = 8L, cv_cycles = 1L)))

---

Compare DID, SDID, MC and TROP on the same panel

Description

Runs a set of panel estimators on a single long panel and returns their average-treatment-effect-on-the-treated (ATT) estimates on a common tidy schema, so applied researchers can compare them at a glance. DID, MC and TROP are computed natively (no external dependencies); SDID and SC are routed through the synthdid package when available, and an alternative MC/IFE can be routed through gsynth. Methods whose optional package is missing, or that do not apply to the design, are skipped with a message.

Usage

panel_compare(
  data,
  outcome,
  treatment,
  unit,
  time,
  methods = c("DID", "SDID", "MC", "TROP", "DIFP"),
  exclude = NULL,
  anchor = "auto",
  se = c("auto", "jackknife", "bootstrap", "placebo", "none"),
  control = trop_control(),
  verbose = FALSE
)

Arguments

data	A long data.frame with one row per unit-time.
outcome, treatment, unit, time	Column names (strings). treatment must be a 0/1 indicator of active treatment in that cell (works for block, staggered, and non-absorbing designs).
methods	Character vector of methods to run. Any of "DID", "SDID", "SC", "MC", "DIFP", "TROP", "gsynth", "augsynth", "CS". Defaults to the native engines plus SDID: c("DID", "SDID", "MC", "TROP", "DIFP").
exclude	Optional character vector of methods to drop from methods (after defaults are applied). Convenient for running "everything except one", e.g. exclude = "DIFP". Unknown names are ignored with a warning.
anchor	Weight anchoring for TROP; see trop().
se	Standard-error method for the native engines; see trop().
control	A list of solver/CV settings from trop_control().
verbose	Logical; print CV progress.

Value

An object of class cf_comparison: a list with the tidy table att (class cf_att_tbl), per-method counterfactual matrices counterfactual, the native fit objects fits, and the reshaped panel.

See Also

trop(), autoplot.cf_comparison(), plot_counterfactual()

Examples

df <- sim_panel(N = 25, T = 14, n_treated = 4, t0 = 10, seed = 3)
cmp <- panel_compare(df, "y", "w", "id", "t",
                     methods = c("DID", "MC", "TROP"), se = "none",
                     control = trop_control(n_cv_cells = 8L, cv_cycles = 1L))
cmp$att

---

Out-of-sample RMSE across panel estimators

Description

Compares estimators by how well each does on held-out control data, following the "random blocks" placebo idea of the doubly/triply robust panel estimator paper. Two scoring rules are available:

Usage

panel_rmse(
  data,
  outcome,
  treatment,
  unit,
  time,
  methods = c("DID", "SC", "SDID", "MC", "TROP", "DIFP"),
  exclude = NULL,
  metric = c("placebo", "prediction"),
  horizon = 10L,
  n_pseudo = 10L,
  n_runs = 10L,
  control = trop_control(),
  seed = NULL,
  verbose = FALSE
)

Arguments

data	A long data.frame, one row per unit-time.
outcome, treatment, unit, time	Column names (strings).
methods	Methods to compare; subset of "DID", "MC", "TROP", "DIFP", "SDID", "SC", "gsynth", "augsynth", "CS". Defaults to c("DID", "SC", "SDID", "MC", "TROP", "DIFP").
exclude	Optional character vector of methods to drop from methods (e.g. exclude = "DIFP"). Unknown names are ignored with a warning.
metric	"placebo" (placebo-ATT RMSE, all methods) or "prediction" (per-cell held-out RMSE, native methods "DID"/"MC"/"TROP" only; other methods, including "DIFP", are ignored for this metric).
horizon	Number of final periods held out per placebo cohort.
n_pseudo	Number of placebo (pseudo-treated) control units per run.
n_runs	Number of placebo runs to average over.
control	A list of solver/CV controls from trop_control().
seed	Optional integer seed for reproducible placebo draws.
verbose	Logical; print progress.

Details

• metric = "placebo" (default): in each run a random set of control units is given a placebo block treatment in the final horizon periods (true effect zero), every method is estimated on that control-only panel, and the score is sqrt(mean(ATT^2)) across runs. This works for every method – native (DID, MC, TROP) and wrapped (SDID/SC via synthdid, gsynth, augsynth, CS = Callaway & Sant'Anna via did).

• metric = "prediction": per-cell one-step-ahead held-out RMSE (the paper's Table-31 style). Implemented for the native methods (DID, MC, TROP) only.

Lower is better. Native methods are tuned once on the real data (DID is parameter-free; MC selects lambda_nn; TROP selects the full triplet by cross-validation) and reused across runs. Wrapped methods are skipped with a note when their package is missing or the design does not apply.

Value

A cf_rmse_tbl (a data.frame) with one row per method and columns method, rmse, rmse_se, n_runs, engine, note.

References

Athey, S., Imbens, G. W., Qu, Z., & Viviano, D. (2025/2026). Triply/Doubly Robust Panel Estimators.

See Also

panel_compare(), autoplot.cf_rmse_tbl()

Examples

df <- sim_panel(N = 20, T = 12, n_treated = 4, t0 = 9, att = 2, seed = 1)
r <- panel_rmse(df, "y", "w", "id", "t",
                methods = c("DID", "TROP"),
                horizon = 2, n_pseudo = 3, n_runs = 2,
                control = trop_control(n_cv_cells = 8L, cv_cycles = 1L),
                seed = 1)
r
autoplot(r)

---

Plot observed vs predicted counterfactual trajectories

Description

For each native engine in a comparison (DID, MC, TROP), draws the average outcome over the treated units against the predicted control counterfactual, to show how the methods extrapolate through the post-treatment period.

Usage

plot_counterfactual(x, methods = NULL)

Arguments

x	A cf_comparison from panel_compare().
methods	Optional subset of methods to draw.

Value

A ggplot2 object.

---

Plot one or both estimation-RMSE curves

Description

Plot one or both estimation-RMSE curves

Usage

## S3 method for class 'cf_rmse_curve'
plot(x, log_y = TRUE, file = NULL, width = 8, height = 5, ...)

## S3 method for class 'cf_rmse_curves'
plot(
  x,
  combined = FALSE,
  log_y = TRUE,
  file = NULL,
  width = NULL,
  height = NULL,
  ...
)

Arguments

x	A cf_rmse_curve or cf_rmse_curves.
log_y	Logical; log10 y axis (default TRUE).
file	Optional path. If given, the figure is written to a PNG at width x height inches; for separate panels two files are written with -control/-pre suffixes. If NULL, draws to the current device.
width, height	Figure size in inches. Defaults are deliberately generous (single 8x5; combined 12x5).
...	Unused.
combined	For cf_rmse_curves: FALSE (default) draws the two panels separately; TRUE draws them side by side (paper-style).

Value

The input, invisibly.

---

Estimation-RMSE curve over one design dimension

Description

Sweeps one design dimension – the number of control units ("n_control") or the number of pre-treatment periods ("n_pre") – and, at each value, runs a semi-synthetic Monte Carlo: panels are drawn from a latent factor model in which treatment is selected on the factor loadings (so plain DID/TWFE is biased), a known constant effect att is imposed, every estimator is run, and the estimation RMSE against the known truth is recorded. The result is the data behind the paper-style "RMSE vs. N_control / T_pre" line plot, one line per estimator. With a dense values grid and enough n_runs the curves are smooth, as in the paper.

Usage

rmse_curve(
  vary = c("n_control", "n_pre"),
  values = NULL,
  n_runs = 500L,
  methods = c("DID", "SDID", "SC", "MC", "DIFP", "TROP"),
  exclude = NULL,
  n_control = 60L,
  n_treated = 8L,
  n_pre = 16L,
  n_post = 6L,
  rank = 4L,
  att = 2,
  noise = 1,
  ar = 0.4,
  trend_sd = 0.05,
  anchor = "pooled",
  control = trop_control(),
  seed = 1L,
  parallel = FALSE,
  verbose = FALSE
)

Arguments

vary	Which dimension to sweep: "n_control" or "n_pre".
values	Integer vector of values for the swept dimension. Defaults to seq(20, 45, by = 5) for control units and seq(5, 38, by = 2) for pre-periods. Each value is an actual measured point (its own Monte Carlo); pass a denser/wider grid for smoother or longer curves.
n_runs	Monte Carlo replications per value (higher = smoother; the paper uses ~1000). Default 500. Note: with the dense default grid and six estimators this is a large simulation (thousands of fits per panel); reduce n_runs and/or values for a quick look, or parallelise the outer loop.
methods	Estimators to include; any subset of the panel_compare() methods. Default c("DID", "SDID", "SC", "MC", "DIFP", "TROP"), the six estimators compared in the paper. gsynth, augsynth and CS can be added if those packages are installed.
exclude	Optional character vector of methods to drop from methods (e.g. exclude = "DIFP"). Unknown names are ignored with a warning.
n_control, n_treated, n_pre, n_post	Base design; the dimension named in vary is overridden by values, the rest are held fixed. Defaults give a sizeable panel (60 controls, 8 treated, 16 pre- and 6 post-periods).
rank, att, noise	Number of latent factors, the imposed (true) ATT, and the idiosyncratic-noise scale.
ar	AR(1) coefficient of the idiosyncratic errors (serial correlation).
trend_sd	SD of the heterogeneous unit-specific linear trend slopes (non-parallel trends); treatment is also selected on this slope.
anchor	Estimation anchor for the native ATT ("pooled" by default for speed across the many fits).
control	Solver/CV controls from trop_control().
seed	Base integer seed (each replication uses a distinct offset).
parallel	Logical; if TRUE and the future.apply package is installed, the Monte Carlo tasks are run in parallel honouring the active future plan (e.g. future::plan(future::multisession, workers = 6)). Results are identical to the sequential default because each task is self-seeded. Default FALSE.
verbose	Logical; print progress per swept value.

Details

This is a simulation diagnostic on synthetic data; it does not take a user panel. To run a curve on your own data, draw panels with sim_semisynthetic() and call panel_compare() in a loop.

Value

A cf_rmse_curve (a data.frame) with columns method, x (the swept value), rmse, bias, n_runs. The swept-dimension label is in attr(., "vary").

See Also

rmse_curves(), panel_rmse(), autoplot.cf_rmse_curve()

Examples

# quick look (small grid + few reps); raise n_runs/values for paper quality
cc <- rmse_curve("n_control", values = c(30, 50), n_runs = 2,
                 methods = c("DID", "TROP"),
                 control = trop_control(n_cv_cells = 8L, cv_cycles = 1L))
autoplot(cc)

---

Estimation-RMSE curves over both design dimensions

Description

Convenience wrapper that runs rmse_curve() for both the number of control units and the number of pre-treatment periods and bundles the two curves, so they can be plotted individually (default) or side by side (combined = TRUE), as in the paper's two-panel figure.

Usage

rmse_curves(values_control = NULL, values_pre = NULL, ...)

Arguments

values_control, values_pre	Optional grids for each sweep (see rmse_curve() defaults).
...	Passed to rmse_curve() (n_runs, methods, base design, att, noise, control, seed, parallel, verbose, ...). Set parallel = TRUE (plus a future::plan) to run both sweeps in parallel.

Value

A cf_rmse_curves object: a list with ⁠$n_control⁠ and ⁠$n_pre⁠, each a cf_rmse_curve.

See Also

rmse_curve(), autoplot.cf_rmse_curves()

Examples

# defaults are a large simulation; use a small grid + few reps for a quick look
g <- rmse_curves(values_control = c(30, 50), values_pre = c(8, 14),
                 n_runs = 2, methods = c("DID", "TROP"),
                 control = trop_control(n_cv_cells = 8L, cv_cycles = 1L))
plot(g)                  # two separate figures (default)
# combined = TRUE needs the optional 'patchwork' package:
if (requireNamespace("patchwork", quietly = TRUE))
  plot(g, combined = TRUE) # side-by-side, paper-style

---

Simulate a panel from a low-rank factor model

Description

Generates a long panel whose untreated potential outcomes follow an interactive-fixed-effects (factor) model on top of two-way fixed effects, in the spirit of the data-generating processes in Athey, Imbens, Qu & Viviano (2025). A block treatment is applied to n_treated units from period t0 onward, with a constant additive effect att.

Usage

sim_panel(
  N = 30,
  T = 20,
  n_treated = 5,
  t0 = NULL,
  rank = 3L,
  att = 1,
  noise = 0.5,
  seed = NULL
)

Arguments

N	Number of units.
T	Number of periods.
n_treated	Number of treated units.
t0	First treated period (block design).
rank	Number of latent factors.
att	True treatment effect added to treated cells.
noise	Standard deviation of idiosyncratic noise.
seed	Optional RNG seed.

Value

A long data.frame with columns id, t, y, w, and the noiseless counterfactual y0 (useful for evaluating estimators).

Examples

df <- sim_panel(N = 30, T = 15, n_treated = 5, t0 = 11, seed = 42)
head(df)

---

Build a semi-synthetic panel from real data

Description

Takes a real long panel, uses its outcomes (optionally smoothed through a low-rank-plus-two-way-fixed-effects fit) as the untreated potential outcomes Y(0), and imposes a known treatment effect on a chosen block of units and periods. Because the baseline is real data but the effect is known, the result is a ground-truth benchmark that "closely matches" the real setting – the style of semi-synthetic experiment used to evaluate panel estimators in Athey, Imbens, Qu & Viviano (2026). Pair it with panel_compare() or panel_rmse() to score estimators against the truth.

Usage

sim_semisynthetic(
  data,
  outcome,
  unit,
  time,
  n_treated,
  t0 = NULL,
  att = 1,
  effect = NULL,
  baseline = c("observed", "lowrank"),
  lambda_nn = NULL,
  noise = 0,
  seed = NULL
)

Arguments

data	A real long data.frame, one row per unit-time.
outcome, unit, time	Column names (strings).
n_treated	Number of units to assign to the placebo treated group (sampled at random from all units).
t0	First treated period (block design). Defaults to about three quarters of the way through the panel.
att	Constant additive treatment effect imposed on treated cells. Ignored if effect is supplied.
effect	Optional per-treated-period effect: a single number, or a numeric vector of length T - t0 + 1 giving a dynamic effect path.
baseline	"observed" uses the real outcomes directly as Y(0); "lowrank" replaces them with a low-rank + two-way-FE fit (optionally plus resampled residuals scaled by noise) for a smoother synthetic baseline.
lambda_nn	Nuclear-norm penalty for the "lowrank" baseline fit.
noise	For "lowrank", standard-deviation multiplier on resampled residuals added back to the fit (0 = noiseless baseline).
seed	Optional RNG seed.

Value

A long data.frame with columns id, t, y, w, y0 (the imposed untreated potential outcome) and tau (the true effect, 0 off treatment).

See Also

sim_panel(), panel_compare(), panel_rmse()

Examples

real <- sim_panel(N = 40, T = 18, n_treated = 0L, att = 0, seed = 1)
ss <- sim_semisynthetic(real, "y", "id", "t",
                        n_treated = 6, t0 = 14, att = 3, seed = 2)
mean(ss$tau[ss$w == 1])   # true ATT = 3

---

Triply RObust Panel (TROP) estimator

Description

Fits the TROP estimator of Athey, Imbens, Qu & Viviano (2025) on a long panel. TROP combines a low-rank-plus-two-way-fixed-effects outcome model with exponential-decay unit weights (upweighting controls similar to the treated) and time weights (upweighting periods near the treated periods). Penalties are chosen by leave-one-out cross-validation on the control cells. The estimator nests DID/TWFE, matrix completion and synthetic-control-type weighting as special cases.

Usage

trop(
  data,
  outcome,
  treatment,
  unit,
  time,
  lambda = NULL,
  anchor = c("auto", "per_cell", "pooled"),
  se = c("auto", "jackknife", "bootstrap", "placebo", "none"),
  grids = NULL,
  control = trop_control(),
  verbose = FALSE
)

Arguments

data	A long data.frame with one row per unit-time.
outcome, treatment, unit, time	Column names (strings). treatment must be a 0/1 indicator of active treatment in that cell (works for block, staggered, and non-absorbing designs).
lambda	Optional named list list(time=, unit=, nn=) to fix the penalties and skip cross-validation.
anchor	How weights are anchored to treated cells: "per_cell" re-solves eq. (2) with cell-specific weights for every treated cell (faithful to the paper); "pooled" solves once with weights anchored to the treated set (fast); "auto" (default) uses per_cell when there are at most max_cells treated cells and pooled otherwise.
se	Standard-error method: "auto", "jackknife" (leave-one-treated- unit-out; needs >= 2 treated units), "placebo" (assign the treated pattern to controls; for a single treated unit), or "none".
grids	Optional list of penalty grids; see trop_control().
control	A list of solver/CV settings from trop_control().
verbose	Logical; print CV progress.

Value

An object of class trop: a list with the ATT estimate, std.error, conf.low/conf.high, the selected penalties lambda, per-cell effects, the estimated counterfactual matrix, weights, and the reshaped panel.

References

Athey, S., Imbens, G. W., Qu, Z., & Viviano, D. (2025). Triply Robust Panel Estimators. arXiv:2508.21536.

Examples

df <- sim_panel(N = 20, T = 12, n_treated = 4, t0 = 9, seed = 1)
fit <- trop(df, "y", "w", "id", "t", se = "none",
            control = trop_control(n_cv_cells = 8L, cv_cycles = 1L))
fit

---

Control settings for trop()

Description

Control settings for trop()

Usage

trop_control(
  max_iter = 200L,
  tol = 1e-05,
  n_cv_cells = 120L,
  cv_cycles = 2L,
  max_cells = 60L,
  conf_level = 0.95,
  n_boot = 200L,
  boot_ci = c("percentile", "normal"),
  svd = c("truncated", "full"),
  workers = 1L,
  seed = NULL
)

Arguments

max_iter	Maximum solver iterations.
tol	Solver convergence tolerance.
n_cv_cells	Number of control cells sampled for the CV criterion.
cv_cycles	Number of coordinate-descent cycles in penalty selection.
max_cells	Threshold for anchor = "auto" to switch to pooled weights.
conf_level	Confidence level for intervals.
n_boot	Number of replications for the bootstrap standard error (se = "bootstrap").
boot_ci	Bootstrap confidence-interval type: "percentile" or "normal".
svd	Singular-value decomposition used by the soft-impute solver: "truncated" (default) computes only the leading singular triplets with RSpectra when it is installed and the matrix is large enough to benefit, falling back to the full SVD otherwise; "full" always uses the exact base R svd(). The two agree to numerical tolerance; "truncated" is faster on large panels, "full" is used for exact numerical-agreement checks.
workers	Number of parallel workers for the embarrassingly parallel loops (cross-validation cells, and the bootstrap / jackknife / placebo replicates). 1 (default) runs serially. Values ⁠> 1⁠ use future.apply/future when installed (a transient multisession plan is set up and restored automatically); if those packages are missing it falls back to serial with no error. Results are reproducible given seed.
seed	Optional seed for CV cell sampling (reproducibility).

Value

A list of control parameters.

---

TROP estimate on matrix input

Description

A thin, matrix-in wrapper around the TROP working model, written independently from the paper. The matrix-in form is convenient for numerically comparing trop() against other matrix-based implementations on identical inputs. For data-frame input, cross-validated penalty selection, inference and the multi-estimator comparison, use trop() and panel_compare() instead.

Usage

trop_matrix(
  Y,
  W,
  treated_units,
  lambda_unit,
  lambda_time,
  lambda_nn,
  treated_periods,
  control = trop_control()
)

Arguments

Y	N x T outcome matrix.
W	N x T 0/1 treatment matrix (1 = actively treated cell).
treated_units	Integer row indices of the treated units, used to anchor the unit weights.
lambda_unit, lambda_time	Non-negative decay parameters for the unit and time weights.
lambda_nn	Nuclear-norm penalty; use Inf to drop the low-rank term.
treated_periods	Number of final columns treated as the post block, used to build the pre-period mask and the time-distance centre.
control	A list of solver controls from trop_control().

Details

The untreated outcome model is fitted on the control cells with the supplied weights, and the returned effect is the average over treated cells of Y - alpha - beta - L, exactly as in the paper's eq. (2). With lambda_nn = Inf the low-rank term is dropped and the fit is weighted two-way fixed effects, matching the reference to numerical tolerance. With a finite lambda_nn the nuclear-norm term is solved by this package's proximal-gradient routine; because that uses a different parameterisation from the reference's convex solver, finite-penalty results agree in behaviour but not to the last digit.

Value

A single numeric ATT estimate.

References

Athey, S., Imbens, G. W., Qu, Z., & Viviano, D. (2025). Triply Robust Panel Estimators. arXiv:2508.21536.

See Also

trop(), panel_compare()

Examples

df <- sim_panel(N = 15, T = 12, n_treated = 3, t0 = 9, att = 2, seed = 1)
Y <- matrix(0, max(df$id), max(df$t)); W <- Y
for (k in seq_len(nrow(df))) { Y[df$id[k], df$t[k]] <- df$y[k]
  W[df$id[k], df$t[k]] <- df$w[k] }
tu <- which(rowSums(W) > 0)
trop_matrix(Y, W, tu, 0.1, 0.1, Inf, treated_periods = 4)

---

TROP penalty-sensitivity grid (heatmap data)

Description

Sweeps the time penalty $\lambda_{time}$ against the nuclear-norm penalty $\lambda_{nn}$ (holding the unit penalty fixed) and records, at each grid point, both the resulting ATT estimate and the leave-one-out cross-validation loss. This is the data behind the diagnostic heatmap: colour the cells by CV loss to see where the data-driven choice lands, and read the ATT off each cell to judge how sensitive the estimate is to the penalties.

Usage

trop_sensitivity(
  data,
  outcome,
  treatment,
  unit,
  time,
  lambda_time = NULL,
  lambda_nn = NULL,
  lambda_unit = NULL,
  anchor = "pooled",
  control = trop_control(),
  seed = NULL,
  verbose = FALSE
)

Arguments

data	A long data.frame, one row per unit-time.
outcome, treatment, unit, time	Column names (strings).
lambda_time, lambda_nn	Numeric grids for the two swept penalties. Defaults are derived from the data scale; lambda_nn defaults to the finite part of the default grid (the heatmap axis needs finite values).
lambda_unit	The fixed unit penalty. If NULL, it is chosen once by cross-validation and held fixed across the sweep.
anchor	Estimation anchor passed to the ATT computation ("pooled" by default for speed).
control	A list of solver/CV controls from trop_control().
seed	Optional integer seed for CV-cell sampling.
verbose	Logical; print progress.

Value

A cf_trop_grid (a data.frame) with columns lambda_time, lambda_nn, lambda_unit, att, cv_loss. The grid point minimising cv_loss (the data-driven choice) is stored in attr(., "selected").

See Also

trop(), autoplot.cf_trop_grid()

Examples

df <- sim_panel(N = 20, T = 12, n_treated = 4, t0 = 9, att = 2, seed = 1)
g <- trop_sensitivity(df, "y", "w", "id", "t",
                      lambda_time = c(0, 0.1, 0.5), lambda_nn = c(2, 5),
                      control = trop_control(n_cv_cells = 8L, cv_cycles = 1L))
autoplot(g)

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