create.propensity.function function

Creation of propensity fitting function

Creates an propensity function that optionally utilizes cross-fitting

create.propensity.function(
  crossfit = TRUE,
  nfolds.crossfit = 10,
  cv.glmnet.args = NULL
)

Arguments

• crossfit: A logical value indicating whether to use cross-fitting (TRUE) or not (FALSE). Cross-fitting is more computationally intensive, but helps to prevent overfitting, see Chernozhukov, et al. (2018)

• nfolds.crossfit: An integer specifying the number of folds to use for cross-fitting. Must be greater than 1

• cv.glmnet.args: A list of NAMED arguments to pass to the cv.glmnet function. For example, cv.glmnet.args = list(type.measure = "mse", nfolds = 10). See cv.glmnet and glmnet

  for all possible options.

Returns

A function which can be passed to the augment.func argument of the fit.subgroup function.

Examples

library(personalized)

set.seed(123)
n.obs  <- 500
n.vars <- 15
x <- matrix(rnorm(n.obs * n.vars, sd = 3), n.obs, n.vars)

# simulate non-randomized treatment
xbetat   <- 0.5 + 0.5 * x[,7] - 0.5 * x[,9]
trt.prob <- exp(xbetat) / (1 + exp(xbetat))
trt01    <- rbinom(n.obs, 1, prob = trt.prob)

trt      <- 2 * trt01 - 1

# simulate response
# delta below drives treatment effect heterogeneity
delta <- 2 * (0.5 + x[,2] - x[,3] - x[,11] + x[,1] * x[,12] )
xbeta <- x[,1] + x[,11] - 2 * x[,12]^2 + x[,13] + 0.5 * x[,15] ^ 2
xbeta <- xbeta + delta * trt

# continuous outcomes
y <- drop(xbeta) + rnorm(n.obs, sd = 2)

aug.func <- create.augmentation.function(family = "gaussian",
                                         crossfit = TRUE,
                                         nfolds.crossfit = 10,
                                         cv.glmnet.args = list(type.measure = "mae",
                                                               nfolds = 5))

prop.func <- create.propensity.function(crossfit = TRUE,
                                        nfolds.crossfit = 10,
                                        cv.glmnet.args = list(type.measure = "mae",
                                                              nfolds = 5))

subgrp.model <- fit.subgroup(x = x, y = y,
                             trt = trt01,
                             propensity.func = prop.func,
                             augment.func = aug.func,
                             loss   = "sq_loss_lasso",
                             nfolds = 10)    # option for cv.glmnet (for ITR estimation)

summary(subgrp.model)

References

Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters https://arxiv.org/abs/1608.00060

See Also

fit.subgroup for estimating ITRs and create.propensity.function for creation of propensity functions