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Off-Policy Evaluation and Counterfactual Methods in Dynamic Auction Environments

Ritam Guha, Nilavra Pathak

TL;DR

The paper studies off-policy evaluation and counterfactual learning in dynamic auction environments to enable offline assessment of payment policies using logged data, thereby reducing reliance on costly A/B tests. It adapts the Open-Bandit Modules pipeline, learns policy proxies from context, and compares discrete versus continuous OPE, finding that continuous estimators (especially SNDR) yield more accurate evaluations. By leveraging counterfactual scenarios and gradient-based optimization, the authors demonstrate how to identify counterfactually optimal policies (OptPaL) that improve resources and returns without new online experiments. The work proposes an advanced analytics vision—a platform for retrospective policy evaluation, optimal policy learning, and monitoring, with potential integration of structural causal models and large language models to enhance policy assessment in complex, high-dimensional auction settings.

Abstract

Counterfactual estimators are critical for learning and refining policies using logged data, a process known as Off-Policy Evaluation (OPE). OPE allows researchers to assess new policies without costly experiments, speeding up the evaluation process. Online experimental methods, such as A/B tests, are effective but often slow, thus delaying the policy selection and optimization process. In this work, we explore the application of OPE methods in the context of resource allocation in dynamic auction environments. Given the competitive nature of environments where rapid decision-making is crucial for gaining a competitive edge, the ability to quickly and accurately assess algorithmic performance is essential. By utilizing counterfactual estimators as a preliminary step before conducting A/B tests, we aim to streamline the evaluation process, reduce the time and resources required for experimentation, and enhance confidence in the chosen policies. Our investigation focuses on the feasibility and effectiveness of using these estimators to predict the outcomes of potential resource allocation strategies, evaluate their performance, and facilitate more informed decision-making in policy selection. Motivated by the outcomes of our initial study, we envision an advanced analytics system designed to seamlessly and dynamically assess new resource allocation strategies and policies.

Off-Policy Evaluation and Counterfactual Methods in Dynamic Auction Environments

TL;DR

The paper studies off-policy evaluation and counterfactual learning in dynamic auction environments to enable offline assessment of payment policies using logged data, thereby reducing reliance on costly A/B tests. It adapts the Open-Bandit Modules pipeline, learns policy proxies from context, and compares discrete versus continuous OPE, finding that continuous estimators (especially SNDR) yield more accurate evaluations. By leveraging counterfactual scenarios and gradient-based optimization, the authors demonstrate how to identify counterfactually optimal policies (OptPaL) that improve resources and returns without new online experiments. The work proposes an advanced analytics vision—a platform for retrospective policy evaluation, optimal policy learning, and monitoring, with potential integration of structural causal models and large language models to enhance policy assessment in complex, high-dimensional auction settings.

Abstract

Counterfactual estimators are critical for learning and refining policies using logged data, a process known as Off-Policy Evaluation (OPE). OPE allows researchers to assess new policies without costly experiments, speeding up the evaluation process. Online experimental methods, such as A/B tests, are effective but often slow, thus delaying the policy selection and optimization process. In this work, we explore the application of OPE methods in the context of resource allocation in dynamic auction environments. Given the competitive nature of environments where rapid decision-making is crucial for gaining a competitive edge, the ability to quickly and accurately assess algorithmic performance is essential. By utilizing counterfactual estimators as a preliminary step before conducting A/B tests, we aim to streamline the evaluation process, reduce the time and resources required for experimentation, and enhance confidence in the chosen policies. Our investigation focuses on the feasibility and effectiveness of using these estimators to predict the outcomes of potential resource allocation strategies, evaluate their performance, and facilitate more informed decision-making in policy selection. Motivated by the outcomes of our initial study, we envision an advanced analytics system designed to seamlessly and dynamically assess new resource allocation strategies and policies.
Paper Structure (24 sections, 9 equations, 8 figures, 1 table, 1 algorithm)

This paper contains 24 sections, 9 equations, 8 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Overview of the Approach
  3. Problem Description
  4. Evaluation Pipeline
  5. Off-Policy Evaluation
  6. Logged Data Collection
  7. Learning Policy Proxies
  8. New Policy Learning
  9. Experimental Evaluation
  10. Comparing Discrete vs Continuous case for payment
  11. Comparison of Continuous Version of the Estimators
  12. Counterfactual Test of Policies: Y vs Z
  13. Use Case I: Assessing the Accuracy of OPE
  14. Use Case II: Estimating the Impact of Policy Z in Test-1 in Place of Policy X, and Comparison with Counterfactual Test-2
  15. New Policy Learning through OPE Optimization
  16. ...and 9 more sections

Figures (8)

  • Figure 1: Off-Policy Evaluator Hyperparameter Learning and Application.
  • Figure 2: Off-Policy Evaluation Process Flow
  • Figure 4: MAPE Estimate: Discretized Action vs Continuous Action Evaluation
  • Figure 5: MAPE Estimate: Evaluation of Different Continuous Evaluators
  • Figure 6: Relative Lifts for the Actual (Y vs Z) and Simulated (Proxy Y vs Z) Tests
  • ...and 3 more figures