Builds condition probability matrices for Horvitz-Thompson estimation from permutation matrix

Usage

permutations_to_condition_pr_mat(permutations)

Arguments

permutations: A matrix where the rows are units and the columns are different treatment permutations; treated units must be represented with a 1 and control units with a 0

Value

a numeric 2n*2n matrix of marginal and joint condition treatment probabilities to be passed to the condition_pr_mat argument of horvitz_thompson.

Details

This function takes a matrix of permutations, for example from the obtain_permutation_matrix function in randomizr or through simulation and returns a 2n*2n matrix that can be used to fully specify the design for horvitz_thompson estimation. You can read more about these matrices in the documentation for the declaration_to_condition_pr_mat function.

This is done by passing this matrix to the condition_pr_mat argument of

Examples


# Complete randomization
perms <- replicate(1000, sample(rep(0:1, each = 50)))
comp_pr_mat <- permutations_to_condition_pr_mat(perms)

# Arbitrary randomization
possible_treats <- cbind(
  c(1, 1, 0, 1, 0, 0, 0, 1, 1, 0),
  c(0, 1, 1, 0, 1, 1, 0, 1, 0, 1),
  c(1, 0, 1, 1, 1, 1, 1, 0, 0, 0)
)
arb_pr_mat <- permutations_to_condition_pr_mat(possible_treats)
# Simulating a column to be realized treatment
z <- possible_treats[, sample(ncol(possible_treats), size = 1)]
y <- rnorm(nrow(possible_treats))
horvitz_thompson(y ~ z, condition_pr_mat = arb_pr_mat)
#>    Estimate Std. Error   t value  Pr(>|t|) CI Lower CI Upper DF
#> z 0.2434772  0.6767714 0.3597629 0.7190245 -1.08297 1.569925 NA