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

In grf: Generalized Random Forests

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

Description

Provides an optional interface to rank_average_treatment_effect which allows for user-supplied evaluation scores.

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
)

Arguments

DR.scores	A vector with the evaluation set 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 (yielding paired standard errors that account for the correlation between RATE metrics estimated on the same evaluation data). 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).

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.

Examples

# Estimate CATEs with a causal forest.
n <- 2000
p <- 5
X <- matrix(rnorm(n * p), n, p)
W <- rbinom(n, 1, 0.5)
event.probability <- 1 / (1 + exp(2 * (pmax(2 * X[, 1], 0) * W - X[, 2])))
Y <- 1 - rbinom(n, 1, event.probability)

train <- sample(1:n, n / 2)
cf.cate <- causal_forest(X[train, ], Y[train], W[train])

# Predict treatment effects on a held-out test set.
test <- -train
cate.hat <-  predict(cf.cate, X[test, ])$predictions

# Estimate AIPW nuisance components on the held-out test set.
Y.forest.eval <- regression_forest(X[test, ], Y[test], num.trees = 500)
Y.hat.eval <- predict(Y.forest.eval)$predictions
W.forest.eval <- regression_forest(X[test, ], W[test], num.trees = 500)
W.hat.eval <- predict(W.forest.eval)$predictions
cf.eval <- causal_forest(X[test, ], Y[test], W[test],
                         Y.hat = Y.hat.eval,
                         W.hat = W.hat.eval)

# Form doubly robust scores.
tau.hat.eval <- predict(cf.eval)$predictions
debiasing.weights.eval <- (W[test] - W.hat.eval) / (W.hat.eval * (1 - W.hat.eval))
Y.residual.eval <- Y[test] - (Y.hat.eval + tau.hat.eval * (W[test] - 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)

# Form a doubly robust RATE estimate on the held-out test set.
rate <- rank_average_treatment_effect.fit(DR.scores, cate.hat)
rate

# Same as
# rate <- rank_average_treatment_effect(cf.eval, cate.hat)

# In settings where the treatment randomization probabilities W.hat are known, an
# alternative to AIPW scores is to use inverse-propensity weighting (IPW):
# 1(W=1) * Y / W.hat - 1(W=0) * Y / (1 - W.hat).
# Here, W.hat = 0.5, and an IPW-based estimate of RATE is:
IPW.scores <- ifelse(W[test] == 1, Y[test] / 0.5, -Y[test] / 0.5)
rate.ipw <- rank_average_treatment_effect.fit(IPW.scores, cate.hat)
rate.ipw

# IPW-based estimators typically have higher variance. For details on
# score constructions for other causal estimands, please see the RATE paper.

grf documentation built on June 24, 2024, 5:20 p.m.