tests/testthat/test-causalglmnet.R

In Larsvanderlaan/causalGLM: Interpretable and robust causal inference for heterogeneous treatment effects using generalized linear models with targeted machine-learning.

test_that("Testing causalglmnet", {
  set.seed(1500)
  data_list <- sim.CATE(n = 100, p = 50)
  # Confounders
  W <- data_list$W
  # Binary treatment
  A <- data_list$A
  # Outcome (binary in this case)
  Y <- data_list$Y
  data <- data_list$data
  trueCATE <- data_list$beta_CATE
  # True treatment effect of data (is constant)
  print(trueCATE)

  # Let's learn it using semiparametric methods.
  # Lets specify a constant model for the CATE
  formula_CATE <- ~1

  # This will take a few seconds learning_method = HAL is a regularized sparse/smoothness adaptive regression spline algorithm. To make it faster change: learning_method or max_degree_Y0W or num_knots_Y0W or parallel.
  #
  doMC::registerDoMC(5)
  causal_fit <- causalglmnet(formula = formula_CATE, data = data, W = W, A = A, Y = Y, estimand = "CATE", cross_fit = TRUE, constant_variance_CATE = TRUE, parallel = TRUE)
  # We got pretty close!
  coefs <- causal_fit$coefs

  # The intercept model is actually a nonparametric estimate of the ATE
  summary(causal_fit)




  set.seed(1500)
  data_list <- sim.OR(n = 100, p = 50)
  # Confounders
  W <- data_list$W
  # Binary treatment
  A <- data_list$A
  # Outcome (binary in this case)
  Y <- data_list$Y
  data <- data_list$data
  trueOR <- data_list$beta_logRR
  # True conditional OR of data (is constant)


  # Let's learn it using semiparametric methods.
  # Lets specify a constant model for the OR
  formula_OR <- ~1


  doMC::registerDoMC(5)
  causal_fit <- causalglmnet(formula = formula_CATE, data = data, W = W, A = A, Y = Y, estimand = "OR", cross_fit = TRUE, constant_variance_CATE = TRUE, parallel = TRUE)
  # We got pretty close!
  coefs <- causal_fit$coefs

  # The intercept model can be interpreted as a population-average of the conditional odds ratio.
  summary(causal_fit)


  set.seed(1500)

  data_list <- sim.RR(100, 50)
  # Confounders
  W <- data_list$W
  # Binary treatment
  A <- data_list$A
  # Outcome (binary in this case)
  Y <- data_list$Y
  data <- data_list$data


  # True log RR of data (is constant)


  # Let's learn it using semiparametric methods.
  # Lets specify a less constant model for the RR (only first coefficient is nonzero)
  formula_logRR <- ~ 1 + W1 + W2
  doMC::registerDoMC(5)
  # This will take a few seconds.
  causal_fit <- causalglmnet(formula = formula_logRR, data, W = W, A = A, Y = Y, estimand = "RR", parallel = TRUE, verbose = TRUE)
  # We got pretty close!

  summary(causal_fit)
  expect_equal(T, T)
})