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TROP estimator for outcome and treatment matrices

Source: R/trop_matrix.R
trop_matrix.Rd

trop_matrix() is the matrix version of trop(). The rows are units and the columns are periods.

Usage

trop_matrix(
  Y,
  W,
  method = c("local", "global"),
  lambda = NULL,
  grid = trop_grid(),
  cv_cells = NULL,
  n_placebo = 200L,
  placebo_size = 1L,
  max_cycles = 10L,
  se = c("bootstrap", "none"),
  n_boot = 200L,
  resample = c("stratified", "controls"),
  conf_level = 0.95,
  ci = c("normal", "percentile"),
  control = trop_control(),
  seed = NULL,
  X = NULL,
  covariate_method = c("joint", "residualize")
)

Arguments

Y

A numeric matrix of outcomes with one row for each unit and one column for each period. NA shows a cell without an outcome.

W

A matrix of treatment indicators with the values 0 and 1. It must have the same dimensions as Y.

method

Use "local" or "global". Refer to the section Methods.

lambda

The tuning parameters as a named numeric vector or a named list with the names time, unit, and nn. If lambda is NULL, cross-validation selects the tuning parameters from grid.

grid

The tuning grids from trop_grid().

cv_cells

For the local method, the number of untreated cells in a random sample for the leave-one-out criterion. If cv_cells is NULL, the criterion uses all untreated cells, as in Eq. 5 of the paper.

n_placebo, placebo_size

For the global method, the number of placebo sets and the number of untreated units in each set. The defaults are the values of the Python package from the authors: 200 sets of 1 unit.

max_cycles

The maximum number of cycles of the coordinate search.

se

Use "bootstrap" for bootstrap standard errors or "none" for no standard errors.

n_boot

The number of bootstrap draws.

resample

Use "stratified" or "controls". Refer to the section Inference.

conf_level

The confidence level of the interval.

ci

Use "normal" or "percentile". The normal interval is the estimate plus or minus a normal quantile times the bootstrap standard error. The percentile interval uses quantiles of the bootstrap draws. Refer to the section Inference.

control

The solver options from trop_control().

seed

An optional integer seed for the sampled cells, the placebo sets, and the bootstrap. The function does not change the state of the random number generator in the R session.

X

Optional covariates. You can use one of three types. An array has the dimensions of Y and one more dimension for the covariates. A matrix with the dimensions of Y gives one covariate. A named list can contain such matrices. Refer to the section Covariates of trop().

covariate_method

Use "joint" or "residualize". Refer to the section Covariates.

Value

An object of class trop. Refer to trop().

Examples

set.seed(1)
Y <- matrix(rnorm(15 * 10), 15, 10)
W <- matrix(0L, 15, 10)
W[1:3, 8:10] <- 1L
fit <- trop_matrix(Y, W, lambda = c(time = 0, unit = 0, nn = Inf), se = "none",
                   control = trop_control(threads = 1))
coef(fit)
#>      ATT 
#> 0.110928