st-tech / zr-obp

Open Bandit Pipeline: a python library for bandit algorithms and off-policy evaluation
Apache License 2.0
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propensity score estimate #178

Open arita37 opened 2 years ago

arita37 commented 2 years ago

Hello,

In the input dataset, propensity scores needs to be provided, does the the propensity score needs to be calibrated ?

How is the impact of wrong propensity score ? (vs reward level...)

usaito commented 2 years ago

@arita37 Calibrating your pscore estimate might help, but depends on the situation. The following papers explore the effect of calibration in OPE, so might be of your interest:

If you're working on a particular application, you might want to evaluate the effect of calibrated vs non-calibrated pscore estimate on the OPE accuracy using synthetic data that mimic your real data. Should be easily implementable with OBP.

arita37 commented 2 years ago

Ok.

But, if you do the math, no calibrated proba can create significant bias.

Different calibration strategies can be used with different temperatures

On Sep 27, 2022, at 3:58, yuta-saito @.***> wrote:

 @arita37 Calibrating your pscore estimate might help, but depends on the situation. The following papers explore the effect of calibration in OPE, so might be of your interest:

Aniruddh Raghu, Omer Gottesman, Yao Liu, Matthieu Komorowski, Aldo Faisal, Finale Doshi-Velez, Emma Brunskill. Behaviour Policy Estimation in Off-Policy Policy Evaluation: Calibration Matters. https://arxiv.org/abs/1807.01066 Yuta Saito, Takuma Udagawa, Haruka Kiyohara, Kazuki Mogi, Yusuke Narita, Kei Tateno. Evaluating the Robustness of Off-Policy Evaluation. https://arxiv.org/abs/2108.13703. If you're working on a particular application, you might want to evaluate the effect of calibrated vs non-calibrated pscore estimate on the OPE accuracy using synthetic data that mimic your real data. Should be easily implementable with OBP.

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