Find the covariate that best predicts treatment effect heterogeneity

An inquiry handler. For each covariate, regresses the true unit-level effect tau on the covariate cut into intervals, and reports which covariate gives the highest R-squared. Pair it with causal_forest_handler(), whose var_imp column is the estimate of the same quantity.

Usage

best_predictor(data, covariate_names, cuts = 20)

Arguments

data: A data frame with a column tau holding each unit's treatment effect, and the covariates named in covariate_names.
covariate_names: A character vector of covariates to assess.
cuts: Either a numeric vector of two or more unique cut points or a single number (greater than or equal to 2) giving the number of intervals into which each covariate is to be cut. Defaults to 20.

Value

A data frame with one row: inquiry ("best_predictor") and estimand, the position in covariate_names of the best predictor.

Details

See https://book.declaredesign.org/complex-designs.html#discovery-using-causal-forests

Examples


set.seed(343)
dat <- data.frame(A = rnorm(500), B = rnorm(500))
dat$tau <- 1 + dat$A

# The effect varies with A, so A (position 1) is the best predictor
best_predictor(dat, covariate_names = c("A", "B"), cuts = 4)
#>          inquiry estimand
#> 1 best_predictor        1