grf: average_partial_effect – R documentation

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average_partial_effect

Estimate average partial effects using a causal forest

Description

Gets estimates of the average partial effect, in particular the (conditional) average treatment effect (target.sample = all): 1/n sum_i = 1^n Cov[Wi, Yi | X = Xi] / Var[Wi | X = Xi]. Note that for a binary unconfounded treatment, the average partial effect matches the average treatment effect.

Usage

average_partial_effect(
  forest,
  calibrate.weights = TRUE,
  subset = NULL,
  debiasing.weights = NULL,
  num.trees.for.variance = 500
)

Arguments

`forest`	The trained forest.
`calibrate.weights`	Whether to force debiasing weights to match expected moments for 1, W, W.hat, and 1/Var[W\|X].
`subset`	Specifies a subset of the training examples over which we estimate the ATE. 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 estimated by a variance forest.
`num.trees.for.variance`	Number of trees used to estimate Var[Wi \| Xi = x]. Default is 500. (only applies when debiasing.weights = NULL)

Details

If clusters are specified, then each unit gets equal weight by default. For example, if there are 10 clusters with 1 unit each and per-cluster ATE = 1, and there are 10 clusters with 19 units each and per-cluster ATE = 0, then the overall ATE is 0.05 (additional sample.weights allow for custom weighting). If equalize.cluster.weights = TRUE each cluster gets equal weight and the overall ATE is 0.5.

Double robust scores are calculated with a component based on a forest estimate of Var[Wi | Xi = x]. These weights can also be passed manually by specifying debiasing.weights.

Value

An estimate of the average partial effect, along with standard error.

Examples

n <- 2000
p <- 10
X <- matrix(rnorm(n * p), n, p)
W <- rbinom(n, 1, 1 / (1 + exp(-X[, 2]))) + rnorm(n)
Y <- pmax(X[, 1], 0) * W + X[, 2] + pmin(X[, 3], 0) + rnorm(n)
tau.forest <- causal_forest(X, Y, W)
tau.hat <- predict(tau.forest)
average_partial_effect(tau.forest)
average_partial_effect(tau.forest, subset = X[, 1] > 0)

grf

Generalized Random Forests

v1.2.0

GPL-3

Authors

Julie Tibshirani [aut, cre], Susan Athey [aut], Rina Friedberg [ctb], Vitor Hadad [ctb], David Hirshberg [ctb], Luke Miner [ctb], Erik Sverdrup [ctb], Stefan Wager [aut], Marvin Wright [ctb]

Initial release