est_ate: Average Treatment Effect of Treating the Optimal Segment
In Netflix/sherlock: Causal Machine Learning for Segment Discovery and Analysis

Evaluate the average treatment effect (ATE) based on static interventions or within subgroups depending on treatment assignment. Efficient one-step and TML estimators are available for each of these target parameters.

1	est_ate(data_with_rule, ate_param, est_type)

`data_with_rule`	A `data.table` containing the input data, augmented with cross-validated nuisance parameter estimates, an estimate of the CATE, and a treatment rule assigned based on the estimated CATE via `assign_rule`. This input object should be created by successive calls to `set_est_data`, `est_cate`, and `assign_rule` in a sequence, or through a wrapper function that composes these function calls automatically.
`ate_param`	A `character` string (of length one) identifying the target parameter to be estimated. Choices are `"ate_static"` for the ATE attributable to the static intervention contrasting the two treatment options at the population level, `"ate_dopt_ctl"` for the ATE in the subgroup identified as potentially being harmed by treatment, as well as `"ate_dopt_trt"` for the ATE in the subgroup identified as benefiting from treatment.
`est_type`	Specification of either the one-step or TML estimator. See the documentation of `est_effect` for details.

A data.table containing point estimates and the standard error of the estimates of the specified target parameter. The resulting object contains the estimated efficient influence function as an attribute appended to the object via setattr.

Netflix/sherlock documentation built on Dec. 17, 2021, 5:22 a.m.