criteo-uplift-balanced

Criteo Uplift Modeling Dataset, preprocessed and balanced for classification / clustering task

From the website:
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Data description
This dataset is constructed by assembling data resulting from several incrementality tests, a particular randomized trial procedure where a random part of the population is prevented from being targeted by advertising. it consists of 25M rows, each one representing a user with 11 features, a treatment indicator and 2 labels (visits and conversions).

Privacy
For privacy reasons the data has been sub-sampled non-uniformly so that the original incrementality level cannot be deduced from the dataset while preserving a realistic, challenging benchmark. Feature names have been anonymized and their values randomly projected so as to keep predictive power while making it practically impossible to recover the original features or user context.

Fields
Here is a detailed description of the fields (they are comma-separated in the file):

f0, f1, f2, f3, f4, f5, f6, f7, f8, f9, f10, f11: feature values (dense, float)
treatment: treatment group (1 = treated, 0 = control)
conversion: whether a conversion occured for this user (binary, label)
visit: whether a visit occured for this user (binary, label)
exposure: treatment effect, whether the user has been effectively exposed (binary)
Key figures
Format: CSV
Size: 459MB (compressed)
Rows: 25,309,483
Average Visit Rate: .04132
Average Conversion Rate: .00229
Treatment Ratio: .846
Tasks
The dataset was collected and prepared with uplift prediction in mind as the main task. Additionally we can foresee related usages such as but not limited to:

benchmark for causal inference
uplift modeling
interactions between features and treatment
heterogeneity of treatment
benchmark for observational causality methods
Contact
For any question, feel free to contact:

The authors of the paper directly (emails in the paper)
Criteo AI Lab team: http://ailab.criteo.com/contact-us/
Criteo AI Lab twitter account: @CriteoResearch
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We have adapted the dataset to a classification / clustering task. The "label" column was obtained by concatenating the columns "conversion", "visit" and "exposure",
we have then dropped duplicated rows and the rows with "label" "110" and "111", due to having too few samples. We have also randomly sampled 500_000 rows with "label" "000" and removed the
other ones, so the "label" column is a bit more balanced with a proportion of 0.37 "000", 0.34 "010", 0.18 "001" and 0.11 "011". Finally, we have encoded the "label" column
as 0: "000", 1: "001", 2: "010", 3: "011". The original index of the dataset was kept.