• Description:

Criteo Uplift Modeling Dataset

This dataset is released along with the paper: “A Large Scale Benchmark for Uplift Modeling” Eustache Diemert, Artem Betlei, Christophe Renaudin; (Criteo AI Lab), Massih-Reza Amini (LIG, Grenoble INP)

This work was published in: AdKDD 2018 Workshop, in conjunction with KDD 2018.

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).

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

• Additional Documentation: Explore on Papers With Code north_east

• Homepage: https://ailab.criteo.com/criteo-uplift-prediction-dataset/

• Source code: tfds.recommendation.criteo.Criteo

• Versions:

  • 1.0.0: Initial release.
  • 1.0.1 (default): Fixed parsing of fields conversion, visit and exposure.

• Download size: 297.00 MiB

• Dataset size: 3.55 GiB

• Auto-cached (documentation): No

• Splits:

• Feature structure:

FeaturesDict({
    'conversion': bool,
    'exposure': bool,
    'f0': float32,
    'f1': float32,
    'f10': float32,
    'f11': float32,
    'f2': float32,
    'f3': float32,
    'f4': float32,
    'f5': float32,
    'f6': float32,
    'f7': float32,
    'f8': float32,
    'f9': float32,
    'treatment': int64,
    'visit': bool,
})

• Feature documentation:

• Supervised keys (See as_supervised doc): ({'exposure': 'exposure', 'f0': 'f0', 'f1': 'f1', 'f10': 'f10', 'f11': 'f11', 'f2': 'f2', 'f3': 'f3', 'f4': 'f4', 'f5': 'f5', 'f6': 'f6', 'f7': 'f7', 'f8': 'f8', 'f9': 'f9', 'treatment': 'treatment'}, 'visit')

• Figure (tfds.show_examples): Not supported.

• Examples (tfds.as_dataframe):

• Citation:

@inproceedings{Diemert2018,
author = { {Diemert Eustache, Betlei Artem} and Renaudin, Christophe and Massih-Reza, Amini},
title={A Large Scale Benchmark for Uplift Modeling},
publisher = {ACM},
booktitle = {Proceedings of the AdKDD and TargetAd Workshop, KDD, London,United Kingdom, August, 20, 2018},
year = {2018}
}