crossurr (version 1.1.2)

xf_surrogate: A function for estimating the proportion of treatment effect explained using cross-fitting.

Description

A function for estimating the proportion of treatment effect explained using cross-fitting.

Usage

xf_surrogate(
  ds,
  x = NULL,
  s,
  y,
  a,
  K = 5,
  outcome_learners = NULL,
  ps_learners = outcome_learners,
  interaction_model = TRUE,
  trim_at = 0.05,
  outcome_family = gaussian(),
  mthd = "superlearner",
  n_ptb = 0,
  ncores = parallel::detectCores() - 1,
  ...
)

Value

a tibble with columns:

• R: estimate of the proportion of treatment effect explained, equal to 1 - deltahat_s/deltahat.

• R_se standard error for the PTE.

• deltahat_s: residual treatment effect estimate.

• deltahat_s_se: standard error for the residual treatment effect.

• pi_o: estimate of the proportion of overlap.

• R_o: PTE only in the overlap region.

• R_o_se: the standard error for R_o.

• deltahat_s_o: residual treatment effect in overlap region,

• deltahat_s_se_o: standard error for deltahat_s_o.

• deltahat: overall treatment effect estimate.

• deltahat_se: standard error for overall treatment effect estimate.

• delta_diff: difference between the treatment effects, equal to the numerator of PTE.

• dd_se: standard error for delta_diff

Arguments

ds

a data.frame.

x

names of all covariates in ds that should be included to control for confounding (eg. age, sex, etc). Default is NULL.

s

names of surrogates in ds.

y

name of the outcome in ds.

a

treatment variable name (eg. groups). Expect a binary variable made of 1s and 0s.

K

number of folds for cross-fitting. Default is 5.

outcome_learners

string vector indicating learners to be used for estimation of the outcome function (e.g., "SL.ridge"). See the SuperLearner package for details.

ps_learners

string vector indicating learners to be used for estimation of the propensity score function (e.g., "SL.ridge"). See the SuperLearner package for details.

interaction_model

logical indicating whether outcome functions for treated and control should be estimated separately. Default is TRUE.

trim_at

threshold at which to trim propensity scores. Default is 0.05.

outcome_family

default is 'gaussian' for continuous outcomes. Other choice is 'binomial' for binary outcomes.

mthd

selected regression method. Default is 'superlearner', which uses the SuperLearner package for estimation. Other choices include 'lasso' (which uses glmnet), 'sis' (which uses SIS), 'cal' (which uses RCAL).

n_ptb

Number of perturbations. Default is 0 which means asymptotic standard errors are used.

ncores

number of CPUs used for parallel computations. Default is parallel::detectCores()-1

...

additional parameters (in particular for super_learner)

Examples

n <- 300
p <- 50
q <- 2
wds <- sim_data(n = n, p = p)

if(interactive()){
 sl_est <- xf_surrogate(ds = wds,
   x = paste('x.', 1:q, sep =''),
   s = paste('s.', 1:p, sep =''),
   a = 'a',
   y = 'y',
   K = 4,
   trim_at = 0.01,
   mthd = 'superlearner',
   outcome_learners = c("SL.mean","SL.lm", "SL.svm", "SL.ridge"),
   ps_learners = c("SL.mean", "SL.glm", "SL.svm", "SL.lda"),
   ncores = 1)

 lasso_est <- xf_surrogate(ds = wds,
   x = paste('x.', 1:q, sep =''),
   s = paste('s.', 1:p, sep =''),
   a = 'a',
   y = 'y',
   K = 4,
   trim_at = 0.01,
   mthd = 'lasso',
   ncores = 1)
}