# DOLCE: Decomposing Off-Policy Evaluation/Learning into Lagged and Current Effects

arXiv: https://arxiv.org/abs/2505.00961 · HTML: https://mlsanity.com/paper/2505.00961/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 31 of its 66 references that are in the map.
- First posted on arXiv 2 May 2025.

## Builds on (most cited first, 30 of 31)

- [Conservative Q-Learning for Offline Reinforcement Learning](https://mlsanity.com/paper/2006.04779/) (2020)
- [Off-Policy Deep Reinforcement Learning without Exploration](https://mlsanity.com/paper/1812.02900/) (2018)
- [Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction](https://mlsanity.com/paper/1906.00949/) (2019)
- [Doubly Robust Off-policy Value Evaluation for Reinforcement Learning](https://mlsanity.com/paper/1511.03722/) (2015)
- [Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble](https://mlsanity.com/paper/2110.01548/) (2021)
- [Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning](https://mlsanity.com/paper/1604.00923/) (2016)
- [Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation](https://mlsanity.com/paper/1810.12429/) (2018)
- [More Robust Doubly Robust Off-policy Evaluation](https://mlsanity.com/paper/1802.03493/) (2018)
- [Empirical Study of Off-Policy Policy Evaluation for Reinforcement Learning](https://mlsanity.com/paper/1911.06854/) (2019)
- [Towards Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling](https://mlsanity.com/paper/1906.03393/) (2019)
- [Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes](https://mlsanity.com/paper/1908.08526/) (2019)
- [Optimal and Adaptive Off-policy Evaluation in Contextual Bandits](https://mlsanity.com/paper/1612.01205/) (2016)
- [Off-policy evaluation for slate recommendation](https://mlsanity.com/paper/1605.04812/) (2016)
- [Doubly robust off-policy evaluation with shrinkage](https://mlsanity.com/paper/1907.09623/) (2019)
- [Balancing Covariates via Propensity Score Weighting](https://mlsanity.com/paper/1609.07494/) (2016)
- [Off-policy Bandits with Deficient Support](https://mlsanity.com/paper/2006.09438/) (2020)
- [Off-Policy Evaluation for Large Action Spaces via Embeddings](https://mlsanity.com/paper/2202.06317/) (2022)
- [Off-Policy Estimation of Long-Term Average Outcomes with Applications to Mobile Health](https://mlsanity.com/paper/1912.13088/) (2019)
- [Understanding the Curse of Horizon in Off-Policy Evaluation via Conditional Importance Sampling](https://mlsanity.com/paper/1910.06508/) (2019)
- [Off-Policy Evaluation for Large Action Spaces via Conjunct Effect Modeling](https://mlsanity.com/paper/2305.08062/) (2023)
- [Policy learning "without" overlap: Pessimism and generalized empirical Bernstein's inequality](https://mlsanity.com/paper/2212.09900/) (2022)
- [Optimal Off-Policy Evaluation from Multiple Logging Policies](https://mlsanity.com/paper/2010.11002/) (2020)
- [Doubly Robust Off-Policy Evaluation for Ranking Policies under the Cascade Behavior Model](https://mlsanity.com/paper/2202.01562/) (2022)
- [Nonparametric causal effects based on incremental propensity score interventions](https://mlsanity.com/paper/1704.00211/) (2017)
- [Off-Policy Evaluation of Ranking Policies under Diverse User Behavior](https://mlsanity.com/paper/2306.15098/) (2023)
- [Causal Estimation for Text Data with (Apparent) Overlap Violations](https://mlsanity.com/paper/2210.00079/) (2022)
- [Towards Assessing and Benchmarking Risk-Return Tradeoff of Off-Policy Evaluation](https://mlsanity.com/paper/2311.18207/) (2023)
- [POTEC: Off-Policy Learning for Large Action Spaces via Two-Stage Policy Decomposition](https://mlsanity.com/paper/2402.06151/) (2024)
- [Policy Learning for Off-Dynamics RL with Deficient Support](https://mlsanity.com/paper/2402.10765/) (2024)
- [Positivity-free Policy Learning with Observational Data](https://mlsanity.com/paper/2310.06969/) (2023)

## Abstract (arXiv)

Off-policy evaluation and learning in contextual bandits use logged interaction data to estimate and optimize the value of a target policy. Most existing methods require sufficient action overlap between the logging and target policies, and violations can bias value and policy gradient estimates. To address this issue, we propose DOLCE (Decomposing Off-policy evaluation/learning into Lagged and Current Effects), which uses only lagged contexts already stored in bandit logs to construct lag-marginalized importance weights and to decompose the objective into a support-robust lagged correction term and a current, model-based term, yielding bias cancellation when the reward-model residual is conditionally mean-zero given the lagged context and action. With multiple candidate lags, DOLCE softly aggregates lag-specific estimates, and we introduce a moment-based training procedure that promotes the desired invariance using only logged lag-augmented data. We show that DOLCE is unbiased in an idealized setting and yields consistent and asymptotically normal estimates with cross-fitting under standard conditions. Our experiments demonstrate that DOLCE achieves substantial improvements in both off-policy evaluation and learning, particularly as the proportion of individuals who violate support increases.