R: Estimate average partial effects using a causal forest

average_partial_effect {grf} | R Documentation

## 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,
      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.
`num.trees.for.variance` | Number of trees used to estimate Var[Wi | Xi = x].

### Details

If clusters are specified, then each cluster gets equal weight. For example, if there are 10 clusters with 1 unit each and per-cluster APE = 1, and there are 10 clusters with 19 units each and per-cluster APE = 0, then the overall APE is 0.5 (not 0.05).

### Value

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

### Examples

    ## Not run:
    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)

    ## End(Not run)

[Package grf version 0.10.3 Index]