# Clip-OGD: An Experimental Design for Adaptive Neyman Allocation in Sequential Experiments

arXiv: https://arxiv.org/abs/2305.17187 · HTML: https://mlsanity.com/paper/2305.17187/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2023.

## Built on by (most cited first, 16 of 16)

- [Semiparametric Efficient Inference in Adaptive Experiments](https://mlsanity.com/paper/2311.18274/) (2023)
- [Optimizing Adaptive Experiments: A Unified Approach to Regret Minimization and Best-Arm Identification](https://mlsanity.com/paper/2402.10592/) (2024)
- [Active Adaptive Experimental Design for Treatment Effect Estimation with Covariate Choices](https://mlsanity.com/paper/2403.03589/) (2024)
- [Learning treatment effects while treating those in need](https://mlsanity.com/paper/2407.07596/) (2024)
- [Optimal Adaptive Experimental Design for Estimating Treatment Effect](https://mlsanity.com/paper/2410.05552/) (2024)
- [Logarithmic Neyman Regret for Adaptive Estimation of the Average Treatment Effect](https://mlsanity.com/paper/2411.14341/) (2024)
- [Allocation Stability and Wald Inference under Variance-Aware UCB](https://mlsanity.com/paper/2412.08843/) (2024)
- [Off-policy estimation with adaptively collected data: the power of online learning](https://mlsanity.com/paper/2411.12786/) (2024)
- [Optimistic Algorithms for Adaptive Estimation of the Average Treatment Effect](https://mlsanity.com/paper/2502.04673/) (2025)
- [Efficient Adaptive Experimentation with Noncompliance](https://mlsanity.com/paper/2505.17468/) (2025)
- [Privacy Preserving Adaptive Experiment Design](https://mlsanity.com/paper/2401.08224/) (2024)
- [Beyond ATE: Multi-Criteria Design for A/B Testing](https://mlsanity.com/paper/2509.05864/) (2025)
- [Minimax and Bayes Optimal Adaptive Experimental Design for Treatment Choice](https://mlsanity.com/paper/2512.08513/) (2025)
- [Adaptive Experimentation for Censored Survival Outcomes](https://mlsanity.com/paper/2605.18459/) (2026)
- [Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation](https://mlsanity.com/paper/2606.31184/) (2026)
- [Tail-Influence Sampling for CVaR Policy Evaluation](https://mlsanity.com/paper/2609.38096/) (2026)

## Abstract (arXiv)

From clinical development of cancer therapies to investigations into partisan bias, adaptive sequential designs have become increasingly popular method for causal inference, as they offer the possibility of improved precision over their non-adaptive counterparts. However, even in simple settings (e.g. two treatments) the extent to which adaptive designs can improve precision is not sufficiently well understood. In this work, we study the problem of Adaptive Neyman Allocation in a design-based potential outcomes framework, where the experimenter seeks to construct an adaptive design which is nearly as efficient as the optimal (but infeasible) non-adaptive Neyman design, which has access to all potential outcomes. Motivated by connections to online optimization, we propose Neyman Ratio and Neyman Regret as two (equivalent) performance measures of adaptive designs for this problem. We present Clip-OGD, an adaptive design which achieves widetildeO(√T) expected Neyman regret and thereby recovers the optimal Neyman variance in large samples. Finally, we construct a conservative variance estimator which facilitates the development of asymptotically valid confidence intervals. To complement our theoretical results, we conduct simulations using data from a microeconomic experiment.