# Additive Control Variates Dominate Self-Normalisation in Off-Policy Evaluation

arXiv: https://arxiv.org/abs/2602.14914 · HTML: https://mlsanity.com/paper/2602.14914/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 31 references that are in the map.
- First posted on arXiv 16 Feb 2026.

## Builds on (most cited first, 12 of 12)

- [Off-policy evaluation for slate recommendation](https://mlsanity.com/paper/1605.04812/) (2016)
- [Offline A/B testing for Recommender Systems](https://mlsanity.com/paper/1801.07030/) (2018)
- [Offline Evaluation of Ranking Policies with Click Models](https://mlsanity.com/paper/1804.10488/) (2018)
- [Evaluating the Robustness of Off-Policy Evaluation](https://mlsanity.com/paper/2108.13703/) (2021)
- [Control Variates for Slate Off-Policy Evaluation](https://mlsanity.com/paper/2106.07914/) (2021)
- [On (Normalised) Discounted Cumulative Gain as an Off-Policy Evaluation Metric for Top-n Recommendation](https://mlsanity.com/paper/2307.15053/) (2023)
- [Optimal Baseline Corrections for Off-Policy Contextual Bandits](https://mlsanity.com/paper/2405.05736/) (2024)
- [Ad-load Balancing via Off-policy Learning in a Content Marketplace](https://mlsanity.com/paper/2309.11518/) (2023)
- [Safe Deployment for Counterfactual Learning to Rank with Exposure-Based Risk Minimization](https://mlsanity.com/paper/2305.01522/) (2023)
- [Multi-Objective Recommendation via Multivariate Policy Learning](https://mlsanity.com/paper/2405.02141/) (2024)
- [Practical Bandits: An Industry Perspective](https://mlsanity.com/paper/2302.01223/) (2023)
- [Delta-rm OPE: Off-Policy Estimation with Pairs of Policies](https://mlsanity.com/paper/2405.10024/) (2024)

## Built on by (most cited first, 2 of 2)

- [Unifying On- and Off-Policy Variance Reduction Methods](https://mlsanity.com/paper/2603.08370/) (2026)
- [Accelerating A/B-Tests with Counterfactual Estimation: Reducing Variance through Policy Overlap](https://mlsanity.com/paper/2607.14604/) (2026)

## Abstract (arXiv)

Off-policy evaluation (OPE) is essential for assessing ranking and recommendation systems without costly online interventions. Self-Normalised Inverse Propensity Scoring (SNIPS) is a standard tool for variance reduction in OPE, leveraging a multiplicative control variate. Recent advances in off-policy learning suggest that additive control variates (baseline corrections) may offer superior performance, yet theoretical guarantees for evaluation are lacking. This paper provides a definitive answer: we prove that β^star-IPS, an estimator with an optimal additive baseline, asymptotically dominates SNIPS in Mean Squared Error. By analytically decomposing the variance gap, we show that SNIPS is asymptotically equivalent to using a specific -- but generally sub-optimal -- additive baseline. Our results theoretically justify shifting from self-normalisation to optimal baseline corrections for both ranking and recommendation.