# Adaptive Experimental Design and Counterfactual Inference

arXiv: https://arxiv.org/abs/2210.14369 · HTML: https://mlsanity.com/paper/2210.14369/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 16 references that are in the map.
- First posted on arXiv 25 Oct 2022.

## Builds on (most cited first, 9 of 9)

- [On the Complexity of Best Arm Identification in Multi-Armed Bandit Models](https://mlsanity.com/paper/1407.4443/) (2014)
- [Time-uniform, nonparametric, nonasymptotic confidence sequences](https://mlsanity.com/paper/1810.08240/) (2018)
- [lil' UCB : An Optimal Exploration Algorithm for Multi-Armed Bandits](https://mlsanity.com/paper/1312.7308/) (2013)
- [On the bias, risk and consistency of sample means in multi-armed bandits](https://mlsanity.com/paper/1902.00746/) (2019)
- [Bridging the gap between regret minimization and best arm identification, with application to A/B tests](https://mlsanity.com/paper/1810.04088/) (2018)
- [Contextual Multi-Armed Bandits for Causal Marketing](https://mlsanity.com/paper/1810.01859/) (2018)
- [Instance-optimal PAC Algorithms for Contextual Bandits](https://mlsanity.com/paper/2207.02357/) (2022)
- [Bayesian Meta-Prior Learning Using Empirical Bayes](https://mlsanity.com/paper/2002.01129/) (2020)
- [Seeker: Real-Time Interactive Search](https://mlsanity.com/paper/1905.13125/) (2019)

## Built on by (most cited first, 5 of 5)

- [Neural Insights for Digital Marketing Content Design](https://mlsanity.com/paper/2302.01416/) (2023)
- [Best of Three Worlds: Adaptive Experimentation for Digital Marketing in Practice](https://mlsanity.com/paper/2402.10870/) (2024)
- [A Data-Driven State Aggregation Approach for Dynamic Discrete Choice Models](https://mlsanity.com/paper/2304.04916/) (2023)
- [Bounded (O(1)) Regret Recommendation Learning via Synthetic Controls Oracle](https://mlsanity.com/paper/2301.12571/) (2023)
- [Counterfactual Evaluation of Ads Ranking Models through Domain Adaptation](https://mlsanity.com/paper/2409.19824/) (2024)

## Abstract (arXiv)

Adaptive experimental design methods are increasingly being used in industry as a tool to boost testing throughput or reduce experimentation cost relative to traditional A/B/N testing methods. This paper shares lessons learned regarding the challenges and pitfalls of naively using adaptive experimentation systems in industrial settings where non-stationarity is prevalent, while also providing perspectives on the proper objectives and system specifications in these settings. We developed an adaptive experimental design framework for counterfactual inference based on these experiences, and tested it in a commercial environment.