causal_forest: Trains a causal forest that can be used to estimate...

In till-tietz/rcf: Heterogenous Treatment Effect Estimation Using Random Forests

Trains a causal forest that can be used to estimate conditional average treatment effects.

Description

Trains a causal forest that can be used to estimate conditional average treatment effects.

Usage

causal_forest(
  n_trees,
  data,
  outcome,
  covariates,
  treat,
  minsize = 5,
  alpha = 0.05,
  feature_fraction = 0.3,
  sample_fraction = 0.5,
  honest_split = TRUE,
  honesty_fraction = 0.5
)

Arguments

n_trees	number of trees to grow as numeric vector.
data	a data frame with predictor, treatment assignment and outcome variables.
outcome	name of the outcome variable as character vector.
covariates	names of the predictors as character vector.
treat	name of the treatment indicator variable as character vector (treatment indicator must be binary numeric 1 = treatment, 0 = control with no NA).
minsize	minimum number of treatment and control observations that must be present in each split as numeric vector. Default 5.
alpha	weighting of cost function numeric vector between 0-1. weights closer to 1 put more emphasis on maximizing heterogeneity. weights closer to 0 put more weight on precisely estimating treatment effects. Default 0.05.
feature_fraction	fraction of total number of predictors to use in fitting each tree as numeric vector between 0-1. Default 0.3.
sample_fraction	fraction of observations to fit each tree with. Default 0.5.
honest_split	enables honest splitting. Default TRUE.
honesty_fraction	fraction of data to be used for honest estimation as numeric vector between 0-1. Default 0.5.

Value

list of causal trees.

till-tietz/rcf documentation built on May 7, 2023, 10:23 p.m.