R: Local linear forest tuning

tune_ll_causal_forest {grf} | R Documentation

## Local linear forest tuning

### Description

Finds the optimal ridge penalty for local linear causal prediction.

### Usage

    tune_ll_causal_forest(
      forest,
      linear.correction.variables = NULL,
      ll.weight.penalty = FALSE,
      num.threads = NULL,
      lambda.path = NULL
    )

### Arguments

`forest` | The forest used for prediction.
`linear.correction.variables` | Variables to use for local linear prediction. If left null, all variables are used. Default is NULL.
`ll.weight.penalty` | Option to standardize ridge penalty by covariance (TRUE), or penalize all covariates equally (FALSE). Defaults to FALSE.
`num.threads` | Number of threads used in training. If set to NULL, the software automatically selects an appropriate amount.
`lambda.path` | Optional list of lambdas to use for cross-validation.

### Value

A list of lambdas tried, corresponding errors, and optimal ridge penalty lambda.

### Examples

    # Find the optimal tuning parameters.
    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)

    forest <- causal_forest(X, Y, W)
    tuned.lambda <- tune_ll_causal_forest(forest)

    # Use this parameter to predict from a local linear causal forest.
    predictions <- predict(forest, linear.correction.variables = 1:p,
                           ll.lambda = tuned.lambda$lambda.min)

[Package grf version 1.2.0 Index]