rank_average_treatment_effect() R function from [grf]

Estimate a Rank-Weighted Average Treatment Effect (RATE).

Consider a rule $S(X_i)$ assigning scores to units in decreasing order of treatment prioritization. In the case of a forest with binary treatment, we provide estimates of the following, where 1/n <= q <= 1 represents the fraction of treated units:

The Rank-Weighted Average Treatment Effect (RATE): $\int_{0}^{1} alpha(q) TOC(q; S) dq$ , where alpha is a weighting method corresponding to either AUTOC or QINI.
The Targeting Operator Characteristic (TOC): $E[Y_i(1) - Y_i(0) | F(S(X_i)) \geq 1 - q] - E[Y_i(1) - Y_i(0)]$ , where $F(\cdot)$ is the distribution function of $S(X_i)$ .

The Targeting Operator Characteristic (TOC) is a curve comparing the benefit of treating only a certain fraction q of units (as prioritized by $S(X_i)$ ), to the overall average treatment effect. The Rank-Weighted Average Treatment Effect (RATE) is a weighted sum of this curve, and is a measure designed to identify prioritization rules that effectively targets treatment (and can thus be used to test for the presence of heterogeneous treatment effects).


rank_average_treatment_effect(
  forest,
  priorities,
  target = c("AUTOC", "QINI"),
  q = seq(0.1, 1, by = 0.1),
  R = 200,
  subset = NULL,
  debiasing.weights = NULL,
  compliance.score = NULL,
  num.trees.for.weights = 500
)

Arguments

forest: The evaluation set forest.
priorities: Treatment prioritization scores S(Xi) for the units used to train the evaluation forest. 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 forest training data.
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.
subset: Specifies subset of the training examples over which we estimate the RATE. WARNING: For valid statistical performance, the subset should be defined only using features Xi, not using the treatment Wi or the outcome Yi.
debiasing.weights: A vector of length n (or the subset length) of debiasing weights. If NULL (default) these are obtained via the appropriate doubly robust score construction, e.g., in the case of causal_forests with a binary treatment, they are obtained via inverse-propensity weighting.
compliance.score: Only used with instrumental forests. An estimate of the causal effect of Z on W, i.e., Delta(X) = E[W | X, Z = 1] - E[W | X, Z = 0], which can then be used to produce debiasing.weights. If not provided, this is estimated via an auxiliary causal forest.
num.trees.for.weights: In some cases (e.g., with causal forests with a continuous treatment), we need to train auxiliary forests to learn debiasing weights. This is the number of trees used for this task. Note: this argument is only used when debiasing.weights = NULL.

Returns

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


# Simulate a simple medical example with a binary outcome and heterogeneous treatment effects.
# We're imagining that the treatment W decreases the risk of getting a stroke for some units,
# while having no effect on the other units (those with X1 < 0).
n <- 2000
p <- 5
X <- matrix(rnorm(n * p), n, p)
W <- rbinom(n, 1, 0.5)
stroke.probability <- 1 / (1 + exp(2 * (pmax(2 * X[, 1], 0) * W - X[, 2])))
Y.stroke <- rbinom(n, 1, stroke.probability)

# We'll label the outcome Y such that "large" values are "good" to make interpretation easier.
# With Y=1 ("no stroke") and Y=0 ("stroke"), then an average treatment effect,
# E[Y(1) - Y(0)] = P[Y(1) = 1] - P[Y(0) = 1], quantifies the counterfactual risk difference
# of being stroke-free with treatment over being stroke-free without treatment.
# This will be positive if the treatment decreases the risk of getting a stroke.
Y <- 1 - Y.stroke

# Train a CATE estimator on a training set.
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

# Next, use the RATE metric to assess heterogeneity.

# Fit an evaluation forest for estimating the RATE.
cf.eval <- causal_forest(X[test, ], Y[test], W[test])

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

# Plot the Targeting Operator Characteristic (TOC) curve.
# In this example, the ATE among the units with high predicted CATEs
# is substantially larger than the overall ATE.
plot(rate)

# Get an estimate of the area under the TOC (AUTOC).
rate

# Construct a 95% CI for the AUTOC.
# A significant result suggests that there are HTEs and that the CATE-based prioritization rule
# is effective at stratifying the sample.
# A non-significant result would suggest that either there are no HTEs
# or that the treatment prioritization rule does not predict them effectively.
rate$estimate + 1.96*c(-1, 1)*rate$std.err

# In some applications, we may be interested in other ways to target treatment.
# One example is baseline risk. In our example, we could estimate the probability of getting
# a stroke in the absence of treatment, and then use this as a non-causal heuristic
# to prioritize individuals with a high baseline risk.
# The hope would be that patients with a high predicted risk of getting a stroke,
# also have a high treatment effect.

# We can use the RATE metric to evaluate this treatment prioritization rule.

# First, fit a baseline risk model on the training set control group (W=0).
train.control <- train[W[train] == 0]
rf.risk <- regression_forest(X[train.control, ], Y.stroke[train.control])

# Then, on the test set, predict the baseline risk of getting a stroke.
baseline.risk.hat <- predict(rf.risk, X[test, ])$predictions

# Use RATE to compare CATE and risk-based prioritization rules.
rate.diff <- rank_average_treatment_effect(cf.eval, cbind(cate.hat, baseline.risk.hat))
plot(rate.diff)

# Construct a 95 % CI for the AUTOC and the difference in AUTOC.
rate.diff$estimate + data.frame(lower = -1.96 * rate.diff$std.err,
                                upper = 1.96 * rate.diff$std.err,
                                row.names = rate.diff$target)

References

Yadlowsky, Steve, Scott Fleming, Nigam Shah, Emma Brunskill, and Stefan Wager. "Evaluating Treatment Prioritization Rules via Rank-Weighted Average Treatment Effects." arXiv preprint arXiv:2111.07966, 2021.

rank_average_treatment_effect function

Estimate a Rank-Weighted Average Treatment Effect (RATE).

Arguments

Returns

Examples

References

See Also