Importance Sampling with Unequal Support

TL;DR

This paper introduces a novel importance sampling method that significantly reduces variance when training and testing data distributions differ, with theoretical analysis and practical applications in personalized medicine.

Contribution

A new importance sampling variant that minimizes variance differences between distributions, with comprehensive bias and variance analysis.

Findings

Reduces variance of importance sampling estimates by orders of magnitude.

Provides theoretical bounds on bias and variance in various settings.

Demonstrates improved policy evaluation in personalized treatment scenarios.

Abstract

Importance sampling is often used in machine learning when training and testing data come from different distributions. In this paper we propose a new variant of importance sampling that can reduce the variance of importance sampling-based estimates by orders of magnitude when the supports of the training and testing distributions differ. After motivating and presenting our new importance sampling estimator, we provide a detailed theoretical analysis that characterizes both its bias and variance relative to the ordinary importance sampling estimator (in various settings, which include cases where ordinary importance sampling is biased, while our new estimator is not, and vice versa). We conclude with an example of how our new importance sampling estimator can be used to improve estimates of how well a new treatment policy for diabetes will work for an individual, using only data from when the individual used a previous treatment policy.