Distributionally Robust Counterfactual Risk Minimization
This work addresses counterfactual decision-making in machine learning, offering a unifying framework that is incremental but improves performance on benchmarks.
The paper tackles the Counterfactual Risk Minimization problem by applying Distributionally Robust Optimization, unifying existing methods and proposing a new robust objective using Kullback-Leibler divergence. It shows that this approach outperforms state-of-the-art methods on four benchmark datasets.
This manuscript introduces the idea of using Distributionally Robust Optimization (DRO) for the Counterfactual Risk Minimization (CRM) problem. Tapping into a rich existing literature, we show that DRO is a principled tool for counterfactual decision making. We also show that well-established solutions to the CRM problem like sample variance penalization schemes are special instances of a more general DRO problem. In this unifying framework, a variety of distributionally robust counterfactual risk estimators can be constructed using various probability distances and divergences as uncertainty measures. We propose the use of Kullback-Leibler divergence as an alternative way to model uncertainty in CRM and derive a new robust counterfactual objective. In our experiments, we show that this approach outperforms the state-of-the-art on four benchmark datasets, validating the relevance of using other uncertainty measures in practical applications.