est_cate: Estimate the Conditional Average Treatment Effect Across...
In Netflix/sherlock: Causal Machine Learning for Segment Discovery and Analysis

A procedure to estimate the conditional average treatment effect (CATE) via a strategy that utilizes a regression of a doubly robust pseudo-outcome derived from the form of the efficient influence function (a key quantity in semiparametric statistics) on the segmentation covariates. Note that the data for this estimation procedure is created based upon the specifications provided in set_est_data, so this function only takes those arguments directly relevant to nuisance parameter estimation.

1 2	est_cate(data_est_spec, cv_folds = 5L, split_type = c("inner", "outer"), ps_learner, or_learner, cate_learner, use_cv_selector = F)

`data_est_spec`	An input `data.table` object created from the input data by `set_est_data`. Note that this data container object has specialized attributes appended to it, so it must be created by that internal utility function.
`cv_folds`	A `numeric` specifying the number of cross-validation folds to be used for sample-splitting when estimating nuisance parameters.
`split_type`	A `character` string (of length one) indicating the sample-splitting "level" at which estimation of the CATE is performed. The choices are "inner", for estimation of the CATE within folds (i.e., at the the same level at which nuisance parameters are estimated), and "outer", in which case the CATE is estimated at the "full-sample" level.
`ps_learner`	Either an instantiated learner object (class inheriting from `Lrnr_base`), from sl3, or a `list` of specifications, or a constant rate between 0 and 1, to be used for estimation of the propensity score (the probability of receiving treatment, conditional on covariates). If `list`: each entry may be an instantiated learner object, or can be a list where one item is an instantiated learner object whose modeling requires specification, and the other item is a list of character vectors, where each vector specifies an interaction term. If constant rate, this rate represents the population probability of being assigned to treatment in an A/B tests. Note that the outcome of this estimation task is strictly binary and that algorithms or ensemble models should be set up accordingly.
`or_learner`	Either an instantiated learner object (class inheriting from `Lrnr_base`), from sl3, or a `list` of specifications, to be used for estimation of the outcome regression (the mean of the response variable, conditional on exposure and covariates). If `list`: each entry can be an instantiated learner object, or can be a list where one item is an instantiated learner object whose modeling requires specification, and the other item is a list of character vectors, where each vector specifies an interaction term.
`cate_learner`	Either an instantiated learner object (class inheriting from `Lrnr_base`), from sl3, or a `list` of specifications, to be used to estimate the CATE, based on a regression of a doubly robust pseudo-outcome on the specified segmentation covariates. If `list`: each entry can be an instantiated learner object, or can be a list where one item is an instantiated learner object whose modeling requires specification, and the other item is a list of character vectors, where each vector specifies an interaction term. Note that the outcome of this estimation task is derived from the other nuisance parameter estimates and should be expected to always be continuous-valued, so algorithms or ensemble models should be set up accordingly. @param use_cv_selector If `TRUE`, then will use Cross-Validation to choose the best among a list of learners when fitting `ps_learner`, `or_learner` or `cate_learner`. If `FALSE` (default), then will use the default metalearner for the outcome type. This argument will not be applied for a `learner` that is not a list but one instantiated learner object.

A data.table of the full data, augmented with additional columns that specify estimates of the nuisance parameters, the doubly robust pseudo-outcome, and the estimated CATE.

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