grf (version 2.3.0)

rank_average_treatment_effect.fit: Fitter function for Rank-Weighted Average Treatment Effect (RATE).

Description

Provides an optional interface to rank_average_treatment_effect which allows for user-supplied doubly robust scores for the evaluation set data.

Usage

rank_average_treatment_effect.fit(
  DR.scores,
  priorities,
  target = c("AUTOC", "QINI"),
  q = seq(0.1, 1, by = 0.1),
  R = 200,
  sample.weights = NULL,
  clusters = NULL
)

Value

A list of class `rank_average_treatment_effect` with elements

• estimate: the RATE estimate.

• std.err: bootstrapped standard error of RATE.

• target: the type of estimate.

• TOC: a data.frame with the Targeting Operator Characteristic curve estimated on grid q, along with bootstrapped SEs.

Arguments

DR.scores

A vector with the evaluation set doubly robust scores.

priorities

Treatment prioritization scores S(Xi) for the units in the evaluation set. Two prioritization rules can be compared by supplying a two-column array or named list of priorities. WARNING: for valid statistical performance, these scores should be constructed independently from the evaluation dataset used to construct DR.scores.

target

The type of RATE estimate, options are "AUTOC" (exhibits greater power when only a small subset of the population experience nontrivial heterogeneous treatment effects) or "QINI" (exhibits greater power when the entire population experience diffuse or substantial heterogeneous treatment effects). Default is "AUTOC".

q

The grid q to compute the TOC curve on. Default is (10%, 20%, ..., 100%).

R

Number of bootstrap replicates for SEs. Default is 200.

sample.weights

Weights given to an observation in estimation. If NULL, each observation is given the same weight. Default is NULL.

clusters

Vector of integers or factors specifying which cluster each observation corresponds to. Default is NULL (ignored).

Examples

# \donttest{
# Train a causal forest to estimate a CATE based priority ranking
n <- 1500
p <- 5
X <- matrix(rnorm(n * p), n, p)
W <- rbinom(n, 1, 0.5)
event.prob <- 1 / (1 + exp(2*(pmax(2*X[, 1], 0) * W - X[, 2])))
Y <- rbinom(n, 1, event.prob)
train <- sample(1:n, n / 2)
cf.priority <- causal_forest(X[train, ], Y[train], W[train])

# Compute a prioritization based on estimated treatment effects.
# -1: in this example the treatment should reduce the risk of an event occuring.
priority.cate <- -1 * predict(cf.priority, X[-train, ])$predictions

# Train a separate CATE estimator for the evaluation set.
Y.forest.eval <- regression_forest(X[-train, ], Y[-train], num.trees = 500)
Y.hat.eval <- predict(Y.forest.eval)$predictions
W.forest.eval <- regression_forest(X[-train, ], W[-train], num.trees = 500)
W.hat.eval <- predict(W.forest.eval)$predictions
cf.eval <- causal_forest(X[-train, ], Y[-train], W[-train],
                         Y.hat = Y.hat.eval,
                         W.hat = W.hat.eval)

# Compute doubly robust scores corresponding to a binary treatment (AIPW).
tau.hat.eval <- predict(cf.eval)$predictions
debiasing.weights.eval <- (W[-train] - W.hat.eval) / (W.hat.eval * (1 - W.hat.eval))
Y.residual.eval <- Y[-train] - (Y.hat.eval + tau.hat.eval * (W[-train] - W.hat.eval))
DR.scores <- tau.hat.eval + debiasing.weights.eval * Y.residual.eval

# Could equivalently be obtained by
# DR.scores <- get_scores(cf.eval)

# Estimate AUTOC.
rate <- rank_average_treatment_effect.fit(DR.scores, priority.cate)

# Same as
rank_average_treatment_effect(cf.eval, priority.cate)
# }