data_example_with_cost: Natural experiment on the effect of a binary treatment on...
In Netflix/sherlock: Causal Machine Learning for Segment Discovery and Analysis
Description
Natural experiment on the effect of a binary treatment on viewing metrics,
where treating units has a cost
Usage
1
Format
A data.table with simulated data from a
quasi-experiment (observational study) with five baseline covariates, a
single timepoint binary treatment, and a continuous-valued outcome (metric
for viewing time). There are 5000 unique units in the dataset, each in a
single row, and 8 columns, described below.
- id
Numeric ID of the observed unit. No repeated measures.
- num_devices
The number of viewing devices recorded for the given
user. A possible segmentation covariate.
- is_p2plus
TODO: FILL IN. A possible segmentation covariate.
- is_newmarket
A binary numeric indicator of whether the user falls
in a region corresponding to a new market.
- baselin_ltv
TODO:
- baseline_viewing
TODO:
- treatment
A binary numeric indicator of whether the unit received
(non-randomly) the intervention of interest.
- outcome_viewing
A continuous-valued measurement of viewing hours,
the outcome of interest. Note that this mimics a metric derived from the
viewing time, not the time itself.
- cost
The cost associated with delivering treatment to the unit.
Netflix/sherlock documentation built on Dec. 17, 2021, 5:22 a.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description
Natural experiment on the effect of a binary treatment on viewing metrics, where treating units has a cost
Usage
1 |
Format
A data.table with simulated data from a
quasi-experiment (observational study) with five baseline covariates, a
single timepoint binary treatment, and a continuous-valued outcome (metric
for viewing time). There are 5000 unique units in the dataset, each in a
single row, and 8 columns, described below.
- id
Numeric ID of the observed unit. No repeated measures.
- num_devices
The number of viewing devices recorded for the given user. A possible segmentation covariate.
- is_p2plus
TODO: FILL IN. A possible segmentation covariate.
- is_newmarket
A binary numeric indicator of whether the user falls in a region corresponding to a new market.
- baselin_ltv
TODO:
- baseline_viewing
TODO:
- treatment
A binary numeric indicator of whether the unit received (non-randomly) the intervention of interest.
- outcome_viewing
A continuous-valued measurement of viewing hours, the outcome of interest. Note that this mimics a metric derived from the viewing time, not the time itself.
- cost
The cost associated with delivering treatment to the unit.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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