# Calculate CATE on a surface in the covariate space Calculates CATE estimates from a causal forest object on a specified surface within the covariate space. ```r CATESurface( forest, continuous_covariates, discrete_covariates, estimate_variance = TRUE, grid = 100, fixed_covariate_fct = median, other_discrete = NULL, max_predict_size = 1e+05, num_threads = 2 ) ``` ## Arguments - `forest`: An object of class `causal_forest`, as returned by causal_forest (). Alternatively, and object of class `regression_forest`, as returned by regression_forest (). - `continuous_covariates`: character, continuous covariates to use for the surface. Must match names in `forest$X.orig`. - `discrete_covariates`: character, discrete covariates to use for the surface. Note that discrete covariates are currently assumed to be one-hot encoded with columns named `{fct_nm}_{lvl_nm}`. Names supplied to discrete_covariates should match `fct_nm`. - `estimate_variance`: boolean, If `TRUE`, the variance of CATE estimates is computed. - `grid`: list, points in which to predict CATE along continuous covariates. Index i in the list should contain a numeric vectors with either a single integer, specifying the number of equally spaced points within the range of the i'th continuous covariate in which to calculate the CATE, or a numeric vector with manually specified points in which to calculate the CATE along the i'th continuous covariate. If all elements of grid specify a number of points, this can be supplied using a numeric vector. If the list is named, the names must match the continuous covariates. grid will be reordered to match the order of continuous_covariates. - `fixed_covariate_fct`: Function applied to covariates not in the sub-surface which returns the fixed value of the covariate used to calculate the CATE. Must be specified in one of the following ways: * A named function, e.g. `mean`. * An anonymous function, e.g. `\(x) x + 1` or `function(x) x + 1`. * A formula, e.g. `~ .x + 1`. You must use `.x` to refer to the first argument. Only recommended if you require backward compatibility with older versions of R. * A string, integer, or list, e.g. `"idx"`, `1`, or `list("idx", 1)` which are shorthand for `\(x) purrr::pluck(x, "idx")`, `\(x) purrr::pluck(x, 1)`, and `\(x) purrr::pluck(x, "idx", 1)` respectively. Optionally supply `.default` to set a default value if the indexed element is `NULL` or does not exist. - `other_discrete`: A data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr) with columns `covs` and `lvl`. Used to specify the level of each discrete covariate to use when calculating the CATE. assumes the use of one-hot encoding. `covs` must contain the name of discrete covariates, and `lvl` the level to use. Set to `NULL` if none of the fixed covariates are discrete using one-hot-encoding. - `max_predict_size`: integer, maximum number of examples to predict at a time. If the surface has more points than max_predict_size, the prediction is split up into an appropriate number of chunks. - `num_threads`: Number of threads used in training. If set to `NULL`, the software automatically selects an appropriate amount. ## Returns Tibble with the predicted CATE's on the specified surface in the covariate space. The tibble has columns for each covariate used to train the input forest, as well as columns output from predict.causal_forest (). ## Examples ```r n <- 1000 p <- 3 X <- matrix(rnorm(n * p), n, p) |> as.data.frame() X_d <- data.frame( X_d1 = factor(sample(1:3, n, replace = TRUE)), X_d2 = factor(sample(1:3, n, replace = TRUE)) ) X_d <- DiscreteCovariatesToOneHot(X_d) X <- cbind(X, X_d) W <- rbinom(n, 1, 0.5) event_prob <- 1 / (1 + exp(2 * (pmax(2 * X[, 1], 0) * W - X[, 2]))) Y <- rbinom(n, 1, event_prob) cf <- grf::causal_forest(X, Y, W) cate_surface <- CATESurface( cf, continuous_covariates = paste0("V", 1:2), discrete_covariates = "X_d1", grid = list( V1 = 10, V2 = -5:5 ), other_discrete = data.frame( covs = "X_d2", lvl = "4" ) ) ``` ## Author(s) KIJA

CATESurface function