R: Intrumental forest

instrumental_forest {grf} | R Documentation

## Intrumental forest

### Description

Trains an instrumental forest that can be used to estimate conditional local average treatment effects tau(X) identified using instruments. Formally, the forest estimates tau(X) = Cov[Y, Z | X = x] / Cov[W, Z | X = x]. Note that when the instrument Z and treatment assignment W coincide, an instrumental forest is equivalent to a causal forest.

### Usage

    instrumental_forest(X, Y, W, Z, Y.hat = NULL, W.hat = NULL,
      Z.hat = NULL, sample.fraction = 0.5, mtry = NULL,
      num.trees = 2000, num.threads = NULL, min.node.size = NULL,
      honesty = TRUE, honesty.fraction = NULL, ci.group.size = 2,
      reduced.form.weight = 0, alpha = 0.05, imbalance.penalty = 0,
      stabilize.splits = TRUE, compute.oob.predictions = TRUE,
      seed = NULL, clusters = NULL, samples_per_cluster = NULL)

### Arguments

`X` | The covariates used in the instrumental regression.
`Y` | The outcome.
`W` | The treatment assignment (may be binary or real).
`Z` | The instrument (may be binary or real).
`Y.hat` | Estimates of the expected responses E[Y | Xi], marginalizing over treatment. If Y.hat = NULL, these are estimated using a separate regression forest.
`W.hat` | Estimates of the treatment propensities E[W | Xi]. If W.hat = NULL, these are estimated using a separate regression forest.
`Z.hat` | Estimates of the instrument propensities E[Z | Xi]. If Z.hat = NULL, these are estimated using a separate regression forest.
`sample.fraction` | Fraction of the data used to build each tree. Note: If honesty = TRUE, these subsamples will further be cut by a factor of honesty.fraction.
`mtry` | Number of variables tried for each split.
`num.trees` | Number of trees grown in the forest. Note: Getting accurate confidence intervals generally requires more trees than getting accurate predictions.
`num.threads` | Number of threads used in training. If set to NULL, the software automatically selects an appropriate amount.
`min.node.size` | A target for the minimum number of observations in each tree leaf. Note that nodes with size smaller than min.node.size can occur, as in the original randomForest package.
`honesty` | Whether to use honest splitting (i.e., sub-sample splitting).
`honesty.fraction` | The fraction of data that will be used for determining splits if honesty = TRUE. Corresponds to set J1 in the notation of the paper. When using the defaults (honesty = TRUE and honesty.fraction = NULL), half of the data will be used for determining splits
`ci.group.size` | The forst will grow ci.group.size trees on each subsample. In order to provide confidence intervals, ci.group.size must be at least 2.
`reduced.form.weight` | Whether splits should be regularized towards a naive splitting criterion that ignores the instrument (and instead emulates a causal forest).
`alpha` | A tuning parameter that controls the maximum imbalance of a split.
`imbalance.penalty` | A tuning parameter that controls how harshly imbalanced splits are penalized.
`stabilize.splits` | Whether or not the instrument should be taken into account when determining the imbalance of a split (experimental).
`compute.oob.predictions` | Whether OOB predictions on training set should be precomputed.
`seed` | The seed for the C++ random number generator.
`clusters` | Vector of integers or factors specifying which cluster each observation corresponds to.
`samples_per_cluster` | If sampling by cluster, the number of observations to be sampled from each cluster when training a tree. If NULL, we set samples_per_cluster to the size of the smallest cluster. If some clusters are smaller than samples_per_cluster, the whole cluster is used every time the cluster is drawn. Note that clusters with less than samples_per_cluster observations get relatively smaller weight than others in training the forest, i.e., the contribution of a given cluster to the final forest scales with the minimum of the number of observations in the cluster and samples_per_cluster.

### Value

A trained instrumental forest object.

[Package grf version 0.10.2 Index]