arXiv 26 Oct 2023 · Statistics — Methodology · 2 citations (OpenAlex)
arXiv:2310.17496 · PDF · DOI · OpenAlex · Extracted main text
In modern recommendation systems, the standard pipeline involves training machine learning models on historical data to predict user behaviors and improve recommendations continuously. However, these data training loops can introduce interference in A/B tests, where data generated by control and treatment algorithms, potentially with different distributions, are combined. To address these challenges, we introduce a novel approach called weighted training. This approach entails training a model to predict the probability of each data point appearing in either the treatment or control data and subsequently applying weighted losses during model training. We demonstrate that this approach achieves the least variance among all estimators that do not cause shifts in the training distributions. Through simulation studies, we demonstrate the lower bias and variance of our approach compared to other methods.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | David Holtz, Jennifer Brennan, and Jean Pouget-Abadie (2023) A study of" symbiosis bias" in a/b tests of recommendation algorithms | 1.000 | 5 | 4 | 100% |
| 2 | Allison JB Chaney, Brandon M Stewart, and Barbara E Engelhardt (2018) How algorithmic confounding in recommendation systems increases homogeneity and decreases utility | 0.644 | 2 | 2 | 100% |
| 3 | Guido W Imbens and Donald B Rubin (2015) Causal inference in statistics, social, and biomedical sciences | 0.644 | 2 | 2 | 100% |
| 4 | Amir H Jadidinejad, Craig Macdonald, and Iadh Ounis (2020) Using exploration to alleviate closed loop effects in recommender systems | 0.644 | 2 | 2 | 100% |
| 5 | Diederik P Kingma and Jimmy Ba (2014) Adam: A method for stochastic optimization | 0.644 | 2 | 2 | 100% |
| 6 | David Holtz, Ruben Lobel, Inessa Liskovich, and Sinan Aral (2020) Reducing interference bias in online marketplace pricing experiments | 0.511 | 2 | 1 | 100% |
| 7 | Yuchen Hu and Stefan Wager (2022) Switchback experiments under geometric mixing | 0.511 | 2 | 1 | 100% |
| 8 | Ramesh Johari, Hannah Li, Inessa Liskovich, and Gabriel Y Weintraub (2022) Experimental design in two-sided platforms: An analysis of bias | 0.511 | 2 | 1 | 100% |
| 9 | Yan Wang and Shan Ba (2023) Producer-side experiments based on counterfactual interleaving designs for online recommender systems | 0.405 | 1 | 1 | 100% |
| 10 | Peter M Aronow and Cyrus Samii (2017) Estimating average causal effects under general interference, with application to a social network experiment | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 65 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | 2501.11996 | 0.405 | 1 | 1 |