Best of Three Worlds: Adaptive Experimentation for Digital Marketing in Practice
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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.
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
16 of 43 references.
References · 16
- 2017A Tutorial on Thompson Samplingcited by 433
- 2014On the Complexity of Best Arm Identification in Multi-Armed Bandit Modelscited by 286
- 2018Time-uniform, nonparametric, nonasymptotic confidence sequencescited by 229
- 2013lil' UCB : An Optimal Exploration Algorithm for Multi-Armed Banditscited by 149
- 2016Simple Bayesian Algorithms for Best Arm Identificationcited by 117
- 2019Confidence Intervals for Policy Evaluation in Adaptive Experimentscited by 95
- 2020Inference for Batched Banditscited by 63
- 2017Accurate Inference for Adaptive Linear Modelscited by 39
- 2019On the bias, risk and consistency of sample means in multi-armed banditscited by 25
- 2018Bridging the gap between regret minimization and best arm identification, with application to A/B testscited by 18
- 2018Contextual Multi-Armed Bandits for Causal Marketingcited by 17
- 2020Bayesian Meta-Prior Learning Using Empirical Bayescited by 14
4 more
- 2019Seeker: Real-Time Interactive Searchcited by 9
- 2021Efficient Inference Without Trading-off Regret in Bandits: An Allocation Probability Test for Thompson Samplingcited by 7
- 2022Adaptive Experimental Design and Counterfactual Inferencecited by 5
- 2023Neural Insights for Digital Marketing Content Designcited by 4
Built on by
Citing papers per year: 2024: 2; 2025: 1; 2026: 1.
4 papers.
- 2025Balancing Interference and Correlation in Spatial Experimental Designs: A Causal Graph Cut Approachcited by 5
- 2024Identifying the Best Arm in the Presence of Global Environment Shiftscited by 1
- 2026Fixed Budget is No Harder Than Fixed Confidence in Best-Arm Identification up to Logarithmic Factorscited by 1
- 2024AExGym: Benchmarks and Environments for Adaptive Experimentationcited by 0
- 2026Fixed Budget is No Harder Than Fixed Confidence in Best-Arm Identification up to Logarithmic Factors3 Feb 2026
- 2025Balancing Interference and Correlation in Spatial Experimental Designs: A Causal Graph Cut Approach26 May 2025
- 2024Identifying the Best Arm in the Presence of Global Environment Shifts22 Aug 2024
- 2024AExGym: Benchmarks and Environments for Adaptive Experimentation8 Aug 2024