| average_treatment_effect {grf} | R Documentation |
Gets estimates of one of the following.
The (conditional) average treatment effect (target.sample = all): sum_i = 1^n E[Y(1) - Y(0) | X = Xi] / n
The (conditional) average treatment effect on the treated (target.sample = treated): sum_Wi = 1 E[Y(1) - Y(0) | X = Xi] / |i : Wi = 1|
The (conditional) average treatment effect on the controls (target.sample = control): sum_Wi = 0 E[Y(1) - Y(0) | X = Xi] / |i : Wi = 0|
The overlap-weighted (conditional) average treatment effect sum_i = 1^n e(Xi) (1 - e(Xi)) E[Y(1) - Y(0) | X = Xi] / sum_i = 1^n e(Xi) (1 - e(Xi)), where e(x) = P[Wi = 1 | Xi = x].
This last estimand is recommended by Li, Morgan, and Zaslavsky (JASA, 2017) in case of poor overlap (i.e., when the propensities e(x) may be very close to 0 or 1), as it doesn't involve dividing by estimated propensities.
average_treatment_effect(forest, target.sample = c("all", "treated",
"control", "overlap"), method = c("AIPW", "TMLE"), subset = NULL)
forest |
The trained forest. |
target.sample |
Which sample to aggregate treatment effects over. |
method |
Method used for doubly robust inference. Can be either augmented inverse-propensity weighting (AIPW), or targeted maximum likelihood estimation (TMLE). |
subset |
Specifies 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. |
If clusters are specified, then each cluster gets equal weight. 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 0.5 (not 0.05).
An estimate of the average treatment effect, along with standard error.
## Not run: # Train a causal forest. n = 50; p = 10 X = matrix(rnorm(n*p), n, p) W = rbinom(n, 1, 0.5) Y = pmax(X[,1], 0) * W + X[,2] + pmin(X[,3], 0) + rnorm(n) c.forest = causal_forest(X, Y, W) # Predict using the forest. X.test = matrix(0, 101, p) X.test[,1] = seq(-2, 2, length.out = 101) c.pred = predict(c.forest, X.test) # Estimate the conditional average treatment effect on the full sample (CATE). average_treatment_effect(c.forest, target.sample = "all") # Estimate the conditional average treatment effect on the treated sample (CATT). # We don't expect much difference between the CATE and the CATT in this example, # since treatment assignment was randomized. average_treatment_effect(c.forest, target.sample = "treated") # Estimate the conditional average treatment effect on samples with positive X[,1]. average_treatment_effect(c.forest, target.sample = "all", X[,1] > 0) ## End(Not run)