Fit an observational causal forest workflow
Usage
fit_observational_forest(
data,
outcome = "outcome",
treatment = "treatment",
covariates,
sample_id = NULL,
candidate = NULL,
num_trees = 2000,
min_node_size = 5,
tree_depth = 3,
tree_minbucket = 100L,
tree_trim_quantiles = c(0.05, 0.95),
seed = NULL
)Arguments
- data
A single analysis
data.frame.- outcome
Outcome column.
- treatment
Treatment assignment column.
- covariates
Baseline covariates for confounding adjustment and heterogeneity discovery.
- sample_id
Optional sample identifier column.
- candidate
Optional treatment-comparison label column.
- num_trees
Number of trees for
grf::causal_forest().- min_node_size
Minimum node size for
grf::causal_forest().- tree_depth
Maximum depth of the explanation tree.
- tree_minbucket
Minimum leaf size of the explanation tree.
- tree_trim_quantiles
Quantiles used to clip extreme effect estimates before fitting the explanation tree.
- seed
Optional random seed.