Doubly Robust Off-Policy Evaluation for Ranking Policies under the Cascade Behavior Model

Doubly Robust Off-Policy Evaluation for Ranking Policies under the Cascade Behavior Model

About

This repository contains the code to replicate the synthetic experiment conducted in the paper "Doubly Robust Off-Policy Evaluation for Ranking Policies under the Cascade Behavior Model" by Haruka Kiyohara, Yuta Saito, Tatsuya Matsuhiro, Yusuke Narita, Nobuyuki Shimizu, and Yasuo Yamamoto, which has been accepted to WSDM2022.

If you find this code useful in your research then please site:

@inproceedings{kiyohara2022doubly,
  author = {Kiyohara, Haruka and Saito, Yuta and Matsuhiro, Tatsuya and Narita, Yusuke and Shimizu, Nobuyuki and Yamamoto, Yasuo},
  title = {Doubly Robust Off-Policy Evaluation for Ranking Policies under the Cascade Behavior Model},
  booktitle = {Proceedings of the 15th International Conference on Web Search and Data Mining},
  pages = {xxx--xxx},
  year = {2022},
}

Dependencies

This repository supports Python 3.7 or newer.

• numpy==1.20.0
• pandas==1.2.1
• scikit-learn==0.24.1
• matplotlib==3.4.3
• obp==0.5.2
• hydra-core==1.0.6

Note that the proposed Cascade-DR estimator is implemented in Open Bandit Pipeline (obp.ope.SlateCascadeDoublyRobust).

Running the code

To conduct the synthetic experiment, run the following commands.

(i) run OPE simulations with varying data size, with the fixed slate size.

python src/main.py setting=n_rounds

(ii), (iii) run OPE simulations with varying slate size and policy similarities, with the fixed data size.

python src/main.py

Once the code is finished executing, you can find the results (squared_error.csv, relative_ee.csv, configuration.csv) in the ./logs/ directory. Lower value is better for squared error and relative estimation error (relative-ee).

Visualize the results

To visualize the results, run the following commands. Make sure that you have executed the above two experiments (by running python src/main.py and python src/main.py setting=default) before visualizing the results.

python src/visualize.py

Then, you will find the following figures (slate size (standard/cascade/independent).png, evaluation policy similarity (standard/cascade/independent).png, data size (standard/cascade/independent).png) in the ./logs/ directory. Lower value is better for the relative-MSE (y-axis).

reward structure	Standard	Cascade	Independent
varying data size (n)
varying slate size (L)
varying evaluation policy similarity (λ)