set_est_data: Inspect the Input Data and Prepare for Estimation Routines
In Netflix/sherlock: Causal Machine Learning for Segment Discovery and Analysis

A utility function to map input data to abstractions useful for estimation. This procedure makes a single copy of the input data, storing it as a data.table for internal usage in later methods.

1 2	set_est_data(data_obs, baseline, exposure, outcome, segment_by, ids = NULL, treatment_cost = NULL)

`data_obs`	Rectangular input data object, whether a `data.frame`, `data.table`, or `tibble`.
`baseline`	A `character` vector specifying the column names in `data_obs` that correspond to the baseline covariates (conditioning set). These variables should temporally precede the exposure and outcome.
`exposure`	A `character` string (of length one) specifying the column in `data_obs` corresponding to the exposure or treatment. This variable should follow those in `baseline` in time but precede the response variable `outcome`.
`outcome`	A `character` string (of length one) specifying the column in `data_obs` corresponding to the response variable.
`segment_by`	A `character` vector specifying the column names in `data_obs` that correspond to the covariates over which segmentation should be performed. This should be a strict subset of `baseline`.
`ids`	A `character` string (of length one) specifying the column in `data_obs` that gives observation-level IDs. The default value of `NULL` assumes that all rows of `data_obs` are independent.
`treatment_cost`	A `character` string (of length one) specifying the column in `data_obs` that gives observation-level treatment cost. The default value of `NULL` indicates that this is not a cost constrained problem or that cost is constant for all units.