mlsanityData to 7 Oct 2026

Designing and Deploying Online Field Experiments

arXiv 1409.3174 ↗First posted 10 Sep 2014cs.HC cs.PL cs.SIcited by 17

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Abstract (arXiv)

Online experiments are widely used to compare specific design alternatives, but they can also be used to produce generalizable knowledge and inform strategic decision making. Doing so often requires sophisticated experimental designs, iterative refinement, and careful logging and analysis. Few tools exist that support these needs. We thus introduce a language for online field experiments called PlanOut. PlanOut separates experimental design from application code, allowing the experimenter to concisely describe experimental designs, whether common "A/B tests" and factorial designs, or more complex designs involving conditional logic or multiple experimental units. These latter designs are often useful for understanding causal mechanisms involved in user behaviors. We demonstrate how experiments from the literature can be implemented in PlanOut, and describe two large field experiments conducted on Facebook with PlanOut. For common scenarios in which experiments are run iteratively and in parallel, we introduce a namespaced management system that encourages sound experimental practice.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Sep 2014.

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Citing papers per year: 2020: 1; 2021: 3; 2022: 3; 2023: 4; 2024: 4; 2025: 2.

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  1. 2021Looper: An end-to-end ML platform for product decisionscited by 9
  2. 2022Policy Evaluation for Temporal and/or Spatial Dependent Experimentscited by 8
  3. 2025On Efficient Estimation of Distributional Treatment Effects under Covariate-Adaptive Randomizationcited by 6
  4. 2022Tisane: Authoring Statistical Models via Formal Reasoning from Conceptual and Data Relationshipscited by 5
  5. 2024Adaptive Experimentation When You Can't Experimentcited by 5
  6. 2024Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reductioncited by 5
  7. 2024Prediction-Guided Active Experimentscited by 5
  8. 2021A Bayesian Model for Online Activity Sample Sizescited by 4
  9. 2023A Two-Part Machine Learning Approach to Characterizing Network Interference in A/B Testingcited by 3
  10. 2025Experimenting, Fast and Slow: Bayesian Optimization of Long-term Outcomes with Online Experimentscited by 2
  11. 2020Distilled Thompson Sampling: Practical and Efficient Thompson Sampling via Imitation Learningcited by 1
  12. 2021Algorithms for Adaptive Experiments that Trade-off Statistical Analysis with Reward: Combining Uniform Random Assignment and Reward Maximizationcited by 1
5 more
  1. 2022Assign Experiment Variants at Scale in Online Controlled Experimentscited by 1
  2. 2023Practical Policy Optimization with Personalized Experimentationcited by 0
  3. 2023Balancing Risk and Reward: An Automated Phased Release Strategycited by 0
  4. 2023Generalized Random Forests using Fixed-Point Treescited by 0
  5. 2024A Nonparametric Bayes Approach to Online Activity Predictioncited by 0