# Fair Effect Attribution in Parallel Online Experiments

arXiv: https://arxiv.org/abs/2210.08338 · HTML: https://mlsanity.com/paper/2210.08338/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 47 references that are in the map.
- First posted on arXiv 15 Oct 2022.

## Builds on (most cited first, 11 of 11)

- [A Survey on Causal Inference](https://mlsanity.com/paper/2002.02770/) (2020)
- [Double/Debiased/Neyman Machine Learning of Treatment Effects](https://mlsanity.com/paper/1701.08687/) (2017)
- [Bounding the Estimation Error of Sampling-based Shapley Value Approximation](https://mlsanity.com/paper/1306.4265/) (2013)
- [Estimation of causal effects with multiple treatments: a review and new ideas](https://mlsanity.com/paper/1701.05132/) (2017)
- [Jointly Learning to Recommend and Advertise](https://mlsanity.com/paper/2003.00097/) (2020)
- [Estimation of Causal Effects of Multiple Treatments in Observational Studies with a Binary Outcome](https://mlsanity.com/paper/2001.06483/) (2020)
- [Statistical Cost Sharing](https://mlsanity.com/paper/1703.03111/) (2017)
- [A Multi-Objective Learning to re-Rank Approach to Optimize Online Marketplaces for Multiple Stakeholders](https://mlsanity.com/paper/1708.00651/) (2017)
- [Patterns of Multistakeholder Recommendation](https://mlsanity.com/paper/1707.09258/) (2017)
- [Multiple Stakeholders in Music Recommender Systems](https://mlsanity.com/paper/1708.00120/) (2017)
- [Revenue, Relevance, Arbitrage and More: Joint Optimization Framework for Search Experiences in Two-Sided Marketplaces](https://mlsanity.com/paper/1905.06452/) (2019)

## Abstract (arXiv)

A/B tests serve the purpose of reliably identifying the effect of changes introduced in online services. It is common for online platforms to run a large number of simultaneous experiments by splitting incoming user traffic randomly in treatment and control groups. Despite a perfect randomization between different groups, simultaneous experiments can interact with each other and create a negative impact on average population outcomes such as engagement metrics. These are measured globally and monitored to protect overall user experience. Therefore, it is crucial to measure these interaction effects and attribute their overall impact in a fair way to the respective experimenters. We suggest an approach to measure and disentangle the effect of simultaneous experiments by providing a cost sharing approach based on Shapley values. We also provide a counterfactual perspective, that predicts shared impact based on conditional average treatment effects making use of causal inference techniques. We illustrate our approach in real world and synthetic data experiments.