Compute HOIF Estimators for ATE

Usage

compute_hoif_estimators(
  residuals,
  B_matrices,
  m = 7,
  backend = "torch",
  pure_R_code = FALSE,
  dtype = NULL
)

Arguments

residuals: A list containing the computed residuals: `R1`, `r1`, `R0`, and `r0`.
B_matrices: A list containing the projection-like basis matrices: `B1` and `B0`.
m: Integer. The maximum order of the HOIF estimator.
backend: Character. The computation backend used by ustat; either "torch" (default) or "numpy". If PyTorch is not available, 'ustats' falls back to "numpy" with a warning.
pure_R_code: Logical. Whether to use a native R implementation. This serves as a fallback when the Python environment used by 'ustats' (via 'reticulate') is not available. Note: The pure R implementation only supports up to the 6th order (m = 6).
dtype: Optional character string passed to ustat controlling the numeric precision of the Python backend: "float32" or "float64". The default NULL selects the precision automatically (float32 on a CUDA GPU, float64 otherwise). Ignored when pure_R_code = TRUE.

Value

A list of HOIF estimators (ATE, HOIF, IIFF) for orders l = 2, ..., m.

Examples

# Pure R example (no Python required), up to order 6
n <- 100
Z <- cbind(1, rnorm(n))
A <- rbinom(n, 1, 0.5)
Y <- A + Z[, 2] + rnorm(n)
residuals <- compute_residuals(A, Y, mu1 = 1 + Z[, 2], mu0 = Z[, 2],
                               pi = rep(0.5, n))
Omega <- compute_gram_inverse(Z, A)
B <- compute_basis_matrix(Z, A, Omega$Omega1, Omega$Omega0)
est <- compute_hoif_estimators(residuals, B, m = 6, pure_R_code = TRUE)
est$ATE
#> [1] -0.04685879 -0.05019283 -0.04800393 -0.04703972 -0.04698073

if (FALSE) { # \dontrun{
# Python backend (requires the 'ustats' Python dependencies), any order
est <- compute_hoif_estimators(residuals, B, m = 7, backend = "torch")
} # }