tests/testthat/test-double_ml_pliv_one_way_cluster.R

In DoubleML: Double Machine Learning in R

context("Unit tests for PLIV with one-way clustering")

lgr::get_logger("mlr3")$set_threshold("warn")

on_cran = !identical(Sys.getenv("NOT_CRAN"), "true")
if (on_cran) {
  test_cases = expand.grid(
    learner = "regr.lm",
    dml_procedure = "dml1",
    score = "partialling out",
    stringsAsFactors = FALSE)
} else {
  test_cases = expand.grid(
    learner = c("regr.lm", "regr.glmnet"),
    dml_procedure = c("dml1", "dml2"),
    score = c("partialling out", "IV-type"),
    stringsAsFactors = FALSE)
}
test_cases[".test_name"] = apply(test_cases, 1, paste, collapse = "_")

set.seed(1234)
N = 25 # number of observations (first dimension)
M = 25 # number of observations (second dimension)
dim_x = 100 # dimension of x
data_one_way = make_pliv_multiway_cluster_CKMS2021(N, M, dim_x,
  omega_X = c(0.25, 0),
  omega_epsilon = c(0.25, 0),
  omega_v = c(0.25, 0),
  omega_V = c(0.25, 0))
data_one_way$cluster_cols = "cluster_var_i"

patrick::with_parameters_test_that("Unit tests for PLIV with one-way clustering:",
  .cases = test_cases, {
    learner_pars = get_default_mlmethod_pliv(learner)

    n_folds = 2
    n_rep = 2
    set.seed(3141)
    if (score == "IV-type") {
      ml_g = learner_pars$ml_g$clone()
    } else {
      ml_g = NULL
    }
    double_mlpliv_obj = DoubleMLPLIV$new(
      data = data_one_way,
      n_folds = n_folds,
      n_rep = n_rep,
      ml_l = learner_pars$ml_l$clone(),
      ml_m = learner_pars$ml_m$clone(),
      ml_r = learner_pars$ml_r$clone(),
      ml_g = ml_g,
      dml_procedure = dml_procedure,
      score = score)

    set.seed(3141)
    double_mlpliv_obj$fit()
    theta_obj = double_mlpliv_obj$coef
    se_obj = double_mlpliv_obj$se

    set.seed(3141)
    if (score == "IV-type") {
      ml_g = learner_pars$ml_g$clone()
    } else {
      ml_g = NULL
    }
    df = as.data.frame(data_one_way$data)
    cluster_var = df$cluster_var_i
    # need to drop variables as x is not explicitly set
    df = df[, !(names(df) %in% c("cluster_var_i", "cluster_var_j"))]
    pliv_hat = dml_pliv(df,
      y = "Y", d = "D", z = "Z",
      n_folds = n_folds,
      ml_l = learner_pars$ml_l$clone(),
      ml_m = learner_pars$ml_m$clone(),
      ml_r = learner_pars$ml_r$clone(),
      ml_g = ml_g,
      dml_procedure = dml_procedure, score = score,
      smpls = double_mlpliv_obj$smpls,
      n_rep = n_rep)

    thetas = rep(NA_real_, n_rep)
    ses = rep(NA_real_, n_rep)
    for (i_rep in 1:n_rep) {
      this_smpl = double_mlpliv_obj$smpls[[i_rep]]
      residuals = compute_pliv_residuals(df,
        y = "Y", d = "D", z = "Z",
        n_folds = n_folds,
        smpls = this_smpl,
        all_preds = pliv_hat$all_preds[[i_rep]])
      y_minus_l_hat = residuals$y_minus_l_hat
      d_minus_r_hat = residuals$d_minus_r_hat
      z_minus_m_hat = residuals$z_minus_m_hat
      y_minus_g_hat = residuals$y_minus_g_hat
      D = df[, "D"]

      if (score == "partialling out") psi_a = -z_minus_m_hat * d_minus_r_hat
      if (score == "IV-type") psi_a = -D * z_minus_m_hat
      if (dml_procedure == "dml2") {
        if (score == "partialling out") psi_b = z_minus_m_hat * y_minus_l_hat
        if (score == "IV-type") psi_b = z_minus_m_hat * y_minus_g_hat
        theta = est_one_way_cluster_dml2(
          psi_a, psi_b,
          cluster_var,
          this_smpl)
      } else {
        theta = pliv_hat$thetas[i_rep]
      }
      if (score == "partialling out") psi = (y_minus_l_hat - d_minus_r_hat * theta) * z_minus_m_hat
      if (score == "IV-type") psi = (y_minus_g_hat - D * theta) * z_minus_m_hat
      var = var_one_way_cluster(
        psi, psi_a,
        cluster_var,
        this_smpl)
      ses[i_rep] = sqrt(var)
      thetas[i_rep] = theta
    }


    theta = stats::median(thetas)
    var_scaling_factor = length(unique(cluster_var))
    se = se_repeated(
      ses * sqrt(var_scaling_factor),
      thetas, theta) / sqrt(var_scaling_factor)
    names(theta) = "D"
    names(se) = "D"

    # at the moment the object result comes without a name
    expect_equal(theta, theta_obj, tolerance = 1e-8)
    expect_equal(se, se_obj, tolerance = 1e-8)
  }
)