# Best of Three Worlds: Adaptive Experimentation for Digital Marketing in Practice

arXiv: https://arxiv.org/abs/2402.10870 · HTML: https://mlsanity.com/paper/2402.10870/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 43 references that are in the map.
- First posted on arXiv 16 Feb 2024.

## Builds on (most cited first, 16 of 16)

- [A Tutorial on Thompson Sampling](https://mlsanity.com/paper/1707.02038/) (2017)
- [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)
- [Simple Bayesian Algorithms for Best Arm Identification](https://mlsanity.com/paper/1602.08448/) (2016)
- [Confidence Intervals for Policy Evaluation in Adaptive Experiments](https://mlsanity.com/paper/1911.02768/) (2019)
- [Inference for Batched Bandits](https://mlsanity.com/paper/2002.03217/) (2020)
- [Accurate Inference for Adaptive Linear Models](https://mlsanity.com/paper/1712.06695/) (2017)
- [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)
- [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)
- [Efficient Inference Without Trading-off Regret in Bandits: An Allocation Probability Test for Thompson Sampling](https://mlsanity.com/paper/2111.00137/) (2021)
- [Adaptive Experimental Design and Counterfactual Inference](https://mlsanity.com/paper/2210.14369/) (2022)
- [Neural Insights for Digital Marketing Content Design](https://mlsanity.com/paper/2302.01416/) (2023)

## Built on by (most cited first, 4 of 4)

- [Balancing Interference and Correlation in Spatial Experimental Designs: A Causal Graph Cut Approach](https://mlsanity.com/paper/2505.20130/) (2025)
- [Identifying the Best Arm in the Presence of Global Environment Shifts](https://mlsanity.com/paper/2408.12581/) (2024)
- [Fixed Budget is No Harder Than Fixed Confidence in Best-Arm Identification up to Logarithmic Factors](https://mlsanity.com/paper/2602.03972/) (2026)
- [AExGym: Benchmarks and Environments for Adaptive Experimentation](https://mlsanity.com/paper/2408.04531/) (2024)

## Abstract (arXiv)

Adaptive experimental design (AED) 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. However, the behavior and guarantees of such methods are not well-understood beyond idealized stationary settings. This paper shares lessons learned regarding the challenges of naively using AED systems in industrial settings where non-stationarity is prevalent, while also providing perspectives on the proper objectives and system specifications in such settings. We developed an AED framework for counterfactual inference based on these experiences, and tested it in a commercial environment.