get_scores.instrumental_forest: Doubly robust scores for estimating the average conditional...

In grf: Generalized Random Forests

Doubly robust scores for estimating the average conditional local average treatment effect.

Description

Given an outcome Y, treatment W and instrument Z, the (conditional) local average treatment effect is tau(x) = Cov[Y, Z | X = x] / Cov[W, Z | X = x]. This is the quantity that is estimated with an instrumental forest. It can be intepreted causally in various ways. Given a homogeneity assumption, tau(x) is simply the CATE at x. When W is binary and there are no "defiers", Imbens and Angrist (1994) show that tau(x) can be interpreted as an average treatment effect on compliers. This doubly robust scores provided here are for estimating tau = E[tau(X)].

Usage

## S3 method for class 'instrumental_forest'
get_scores(
  forest,
  subset = NULL,
  debiasing.weights = NULL,
  compliance.score = NULL,
  num.trees.for.weights = 500,
  ...
)

Arguments

forest	A trained instrumental forest.
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.
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	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.
...	Additional arguments (currently ignored).

Value

A vector of scores.

References

Aronow, Peter M., and Allison Carnegie. "Beyond LATE: Estimation of the average treatment effect with an instrumental variable." Political Analysis 21(4), 2013.

Chernozhukov, Victor, Juan Carlos Escanciano, Hidehiko Ichimura, Whitney K. Newey, and James M. Robins. "Locally robust semiparametric estimation." Econometrica 90(4), 2022.

Imbens, Guido W., and Joshua D. Angrist. "Identification and Estimation of Local Average Treatment Effects." Econometrica 62(2), 1994.

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