Debiased Machine Learning

test_that("ddml_ate computes with a single model", {
  # Simulate small dataset
  nobs <- 200
  X <- cbind(1, matrix(rnorm(nobs*39), nobs, 39))
  D_tld <-  X %*% runif(40) + rnorm(nobs)
  D <- 1 * (D_tld > mean(D_tld))
  y <- D + X %*% runif(40) + rnorm(nobs)
  # Define arguments
  learners <- list(what = ols)
  expect_warning({
    ddml_ate_fit <- ddml_ate(y, D, X,
                             learners = learners,
                             cv_folds = 3,
                             sample_folds = 3,
                             silent = T)
  })
  # Check output with expectations
  expect_equal(length(ddml_ate_fit$ate), 1)
})#TEST_THAT

test_that("ddml_ate computes with a single model and dependence", {
  # Simulate small dataset
  n_cluster <- 200
  nobs <- 500
  X <- cbind(1, matrix(rnorm(n_cluster*39), n_cluster, 39))
  D_tld <-  X %*% runif(40) + rnorm(n_cluster)
  fun <- stepfun(quantile(D_tld, probs = 0.5), c(0, 1))
  D <- fun(D_tld)
  cluster_variable <- sample(1:n_cluster, nobs, replace = TRUE)
  D <- D[cluster_variable, drop = F]
  X <- X[cluster_variable, , drop = F]
  y <- D + X %*% runif(40) + rnorm(nobs)
  # Define arguments
  learners <- list(what = ols)
  expect_warning({
    ddml_ate_fit <- ddml_ate(y, D, X,
                             learners = learners,
                             cluster_variable = cluster_variable,
                             cv_folds = 3,
                             sample_folds = 3,
                             silent = T)
  })
  # Check output with expectations
  expect_equal(length(ddml_ate_fit$ate), 1)
})#TEST_THAT

test_that("ddml_ate computes with an ensemble procedure", {
  # Simulate small dataset
  nobs <- 200
  X <- cbind(1, matrix(rnorm(nobs*39), nobs, 39))
  D_tld <-  X %*% runif(40) + rnorm(nobs)
  D <- 1 * (D_tld > mean(D_tld))
  y <- D + X %*% runif(40) + rnorm(nobs)
  # Define arguments
  learners <- list(list(fun = ols),
                   list(fun = ols))
  # Compute DDML PLM estimator
  expect_warning({
    ddml_ate_fit <- ddml_ate(y, D, X,
                             learners = learners,
                             ensemble_type = "ols",
                             cv_folds = 3,
                             sample_folds = 3,
                             silent = T)
  })
  # Check output with expectations
  expect_equal(length(ddml_ate_fit$ate), 1)
})#TEST_THAT

test_that("ddml_ate computes w/ multiple ensembles + custom weights", {
  # Simulate small dataset
  nobs <- 200
  X <- cbind(1, matrix(rnorm(nobs*39), nobs, 39))
  D_tld <-  X %*% runif(40) + rnorm(nobs)
  D <- 1 * (D_tld > mean(D_tld))
  y <- D + X %*% runif(40) + rnorm(nobs)
  # Define arguments
  learners <- list(list(fun = ols),
                   list(fun = ols))
  # Compute DDML PLM estimator
  expect_warning({
    ddml_ate_fit <- ddml_ate(y, D, X,
                             learners,
                             ensemble_type = c("ols", "nnls",
                                               "singlebest", "average"),
                             cv_folds = 3,
                             custom_ensemble_weights = diag(1, 2),
                             sample_folds = 3,
                             silent = T)
  })
  # Check output with expectations
  expect_equal(length(ddml_ate_fit$ate), 6)
})#TEST_THAT

test_that("ddml_ate computes with multiple ensemble procedures & shortstack", {
  # Simulate small dataset
  nobs <- 200
  X <- cbind(1, matrix(rnorm(nobs*39), nobs, 39))
  D_tld <-  X %*% runif(40) + rnorm(nobs)
  D <- 1 * (D_tld > mean(D_tld))
  y <- D + X %*% runif(40) + rnorm(nobs)
  # Define arguments
  learners <- list(list(fun = ols))
  # Compute DDML PLM estimator
  expect_warning({
    ddml_ate_fit <- ddml_ate(y, D, X,
                             learners,
                             ensemble_type = c("ols", "nnls",
                                               "singlebest", "average"),
                             shortstack = TRUE,
                             cv_folds = 3,
                             sample_folds = 3,
                             silent = T)
  })
  # Check output with expectations
  expect_equal(length(ddml_ate_fit$ate), 4)
})#TEST_THAT

test_that("summary.ddml_ate computes with a single model", {
  # Simulate small dataset
  nobs <- 200
  X <- cbind(1, matrix(rnorm(nobs*39), nobs, 39))
  D_tld <-  X %*% runif(40) + rnorm(nobs)
  D <- 1 * (D_tld > mean(D_tld))
  y <- D + X %*% runif(40) + rnorm(nobs)
  # Define arguments
  learners <- list(what = ols)
  expect_warning({
    ddml_ate_fit <- ddml_ate(y, D, X,
                             learners = learners,
                             cv_folds = 3,
                             sample_folds = 3,
                             silent = T)
  })
  # Compute inference results & test print
  inf_res <- summary(ddml_ate_fit)
  capture_output({print(inf_res)}, print = FALSE)
  # Check output with expectations
  expect_equal(length(inf_res), 4)
})#TEST_THAT

test_that("summary.ddml_ate computes with a single model and dependence", {
  # Simulate small dataset
  n_cluster <- 200
  nobs <- 500
  X <- cbind(1, matrix(rnorm(n_cluster*39), n_cluster, 39))
  D_tld <-  X %*% runif(40) + rnorm(n_cluster)
  fun <- stepfun(quantile(D_tld, probs = 0.5), c(0, 1))
  D <- fun(D_tld)
  cluster_variable <- sample(1:n_cluster, nobs, replace = TRUE)
  D <- D[cluster_variable, drop = F]
  X <- X[cluster_variable, , drop = F]
  y <- D + X %*% runif(40) + rnorm(nobs)
  # Define arguments
  learners <- list(what = ols)
  expect_warning({
    ddml_ate_fit <- ddml_ate(y, D, X,
                             learners = learners,
                             cluster_variable = cluster_variable,
                             cv_folds = 3,
                             sample_folds = 3,
                             silent = T)
  })
  # Compute inference results & test print
  inf_res <- summary(ddml_ate_fit)
  capture_output({print(inf_res)}, print = FALSE)
  # Check output with expectations
  expect_equal(length(inf_res), 4)
})#TEST_THAT

test_that("summary.ddml_ate computes with multiple ensemble procedures", {
  # Simulate small dataset
  nobs <- 200
  X <- cbind(1, matrix(rnorm(nobs*39), nobs, 39))
  D_tld <-  X %*% runif(40) + rnorm(nobs)
  D <- 1 * (D_tld > mean(D_tld))
  y <- D + X %*% runif(40) + rnorm(nobs)
  # Define arguments
  learners <- list(list(fun = ols))
  # Compute DDML PLM estimator
  expect_warning({
    ddml_ate_fit <- ddml_ate(y, D, X,
                             learners,
                             ensemble_type = c("ols", "nnls",
                                               "singlebest", "average"),
                             cv_folds = 3,
                             sample_folds = 3,
                             silent = T)
  })
  # Compute inference results & test print
  inf_res <- summary(ddml_ate_fit)
  capture_output({print(inf_res)}, print = FALSE)
  # Check output with expectations
  expect_equal(length(inf_res), 16)
})#TEST_THAT