Off-Policy Evaluation for Large Action Spaces via Policy Convolution

Developing accurate off-policy estimators is crucial for both evaluating and optimizing for new policies. The main challenge in off-policy estimation is the distribution shift between the logging policy that generates data and the target policy that we aim to evaluate. Typically, techniques for correcting distribution shift involve some form of importance sampling. This approach results in unbiased value estimation but often comes with the trade-off of high variance. Furthermore, importance sampling relies on the common support assumption, which becomes impractical when the action space is large. To address these challenges, we introduce the Policy Convolution (PC) family of estimators for the contextual bandit setting. These methods leverage latent structure within actions---made available through action embeddings---to strategically convolve the logging and target policies. This convolution introduces a unique bias-variance trade-off, that can be controlled via the amount of convolution. Our experiments on synthetic and benchmark datasets demonstrate remarkable mean squared error (MSE) improvements when using PC, especially when either the action space or policy mismatch becomes large, with gains of up to 5-6 orders of magnitude over existing estimators.