Saurabh Sohoney
ABSTRACT
Inverse Propensity Score estimator (IPS) is a basic, unbiased, off-policy evaluation technique to measure the impact of a user-interactive system without serving live traffic. We present our work on applying IPS to real-world settings by addressing some practical challenges, thereby enabling successful policy evaluation. In particular, we show that off-policy evaluation can be impossible in the absence of a complete context and we describe a systematic way of defining the context.
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Digital Library
- L. Li, S. Chen, J. Kleban, and A. Gupta. 2015 a. Counterfactual Estimation and Optimization of Click Metrics in Search Engines: A Case Study WWW '15 Companion. Google ScholarDigital Library
- L. Li, J. Young Kim, and I. Zitouni. 2015 b. Toward Predicting the Outcome of an A/B Experiment for Search Relevance WSDM. Google ScholarDigital Library
- A. Strehl, J. Langford, L. Li, and S. Kakade. 2010. Learning from Logged Implicit Exploration Data. NIPS. Google ScholarDigital Library
- A. Swaminathan and T. Joachims. 2015. The self-normalized estimator for counterfactual learning NIPS. Google ScholarDigital Library
- J. Tang, A. Salem, and L. Huan. 2014. Feature selection for classification: A review. In Data Classification: Algorithms and Applications.Google Scholar
Index Terms
- Handling Confounding for Realistic Off-Policy Evaluation
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