fit_or_mech: Outcome Regression Estimation
In Netflix/sherlock: Causal Machine Learning for Segment Discovery and Analysis

Estimate the outcome regression (OR), Q(A,W), the conditional mean of the outcome/response variable, conditional on both the expsoure and the baseline covariates.

1	fit_or_mech(train_data, valid_data, learner)

`train_data`	A `data.table` containing those observations falling in the training set for a particular cross-validation sample split. This data object is created by the internal call of `est_cate` to `cross_validate` and is (unfortunately) a copy of a subset of the full estimation data.
`valid_data`	A `data.table` containing those observations falling in the holdout (validation) set for a particular cross-validation sample split. This data object is created by the internal call of `est_cate` to `cross_validate` and is (unfortunately) a copy of a subset of the full estimation data.
`learner`	An instantiated learner object, with class inheriting from `Lrnr_base`, from sl3, to be used for estimation of the outcome regression (the mean of the response variable, conditional on exposure and covariates).

A list (as required by cross_validate) containing two slots populated with data.tables for the training and validation data (for a given cross-validation split). Each data.table has three columns, corresponding to estimates of the conditional mean of the outcome under the natural value of the exposure ("QA"), under the control condition ("Q0"), and under the treatment condition ("Q1").

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