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Unbiased Estimation for Total Treatment Effect Under Interference Using Aggregated Dyadic Data (2402.12653v1)

Published 20 Feb 2024 in cs.SI and stat.AP

Abstract: In social media platforms, user behavior is often influenced by interactions with other users, complicating the accurate estimation of causal effects in traditional A/B experiments. This study investigates situations where an individual's outcome can be broken down into the sum of multiple pairwise outcomes, a reflection of user interactions. These outcomes, referred to as dyadic data, are prevalent in many social network contexts. Utilizing a Bernoulli randomized design, we introduce a novel unbiased estimator for the total treatment effect (TTE), which quantifies the difference in population mean when all individuals are assigned to treatment versus control groups. We further explore the bias of our estimator in scenarios where it is impractical to include all individuals in the experiment, a common constraint in online control experiments. Our numerical results reveal that our proposed estimator consistently outperforms some commonly used estimators, underscoring its potential for more precise causal effect estimation in social media environments.

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References (39)
  1. Social networks and cooperation in hunter-gatherers. Nature 481, 7382 (2012), 497–501.
  2. Peter M Aronow and Cyrus Samii. 2012. Estimating average causal effects under general interference. In Summer Meeting of the Society for Political Methodology, University of North Carolina, Chapel Hill, July. 19–21.
  3. Peter M Aronow and Cyrus Samii. 2017. Estimating average causal effects under general interference, with application to a social network experiment. (2017).
  4. Exact p-values for network interference. J. Amer. Statist. Assoc. 113, 521 (2018), 230–240.
  5. Guillaume W Basse and Edoardo M Airoldi. 2015. Optimal model-assisted design of experiments for network correlated outcomes suggests new notions of network balance. arXiv preprint arXiv:1507.00803 (2015).
  6. Guillaume W Basse and Edoardo M Airoldi. 2018. Model-assisted design of experiments in the presence of network-correlated outcomes. Biometrika 105, 4 (2018), 849–858.
  7. Fast unfolding of communities in large networks. Journal of statistical mechanics: theory and experiment 2008, 10 (2008), P10008.
  8. Alex Chin. 2018. Central limit theorems via Stein’s method for randomized experiments under interference. arXiv preprint arXiv:1804.03105 (2018).
  9. Staggered rollout designs enable causal inference under interference without network knowledge. Advances in Neural Information Processing Systems 35 (2022), 7437–7449.
  10. Design and analysis of experiments in networks: Reducing bias from interference. Journal of Causal Inference 5, 1 (2016), 20150021.
  11. Identification and estimation of treatment and interference effects in observational studies on networks. J. Amer. Statist. Assoc. 116, 534 (2021), 901–918.
  12. Estimating causal effects under network interference with Bayesian generalized propensity scores. The Journal of Machine Learning Research 23, 1 (2022), 13101–13161.
  13. Network a/b testing: From sampling to estimation. In Proceedings of the 24th International Conference on World Wide Web. 399–409.
  14. Richard J Hayes and Lawrence H Moulton. 2017. Cluster randomised trials. CRC press.
  15. Average direct and indirect causal effects under interference. Biometrika 109, 4 (2022), 1165–1172.
  16. Guido W Imbens and Donald B Rubin. 2015. Causal inference in statistics, social, and biomedical sciences. Cambridge University Press.
  17. Network experimentation at scale. In Proceedings of the 27th acm sigkdd conference on knowledge discovery & data mining. 3106–3116.
  18. Online controlled experiments at large scale. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. 1168–1176.
  19. Seven rules of thumb for web site experimenters. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. 1857–1866.
  20. Michael P Leung. 2022. Causal inference under approximate neighborhood interference. Econometrica 90, 1 (2022), 267–293.
  21. Shuangning Li and Stefan Wager. 2022. Random graph asymptotics for treatment effect estimation under network interference. The Annals of Statistics 50, 4 (2022), 2334–2358.
  22. Adaptive A/B Test on Networks with Cluster Structures. In International Conference on Artificial Intelligence and Statistics. PMLR, 10836–10851.
  23. Douglas A Luke and Jenine K Harris. 2007. Network analysis in public health: history, methods, and applications. Annu. Rev. Public Health 28 (2007), 69–93.
  24. Charles F Manski. 2013. Identification of treatment response with social interactions. The Econometrics Journal 16, 1 (2013), S1–S23.
  25. Richard Portes and Helene Rey. 2005. The determinants of cross-border equity flows. Journal of international Economics 65, 2 (2005), 269–296.
  26. Testing for arbitrary interference on experimentation platforms. Biometrika 106, 4 (2019), 929–940.
  27. A method for measuring network effects of one-to-one communication features in online a/b tests. arXiv preprint arXiv:1903.08766 (2019).
  28. Using ego-clusters to measure network effects at LinkedIn. arXiv preprint arXiv:1903.08755 (2019).
  29. Model assisted survey sampling. Springer Science & Business Media.
  30. Fredrik Sävje. 2023. Causal inference with misspecified exposure mappings: separating definitions and assumptions. Biometrika (2023), asad019.
  31. Average treatment effects in the presence of unknown interference. Annals of statistics 49, 2 (2021), 673.
  32. Daniel L Sussman and Edoardo M Airoldi. 2017. Elements of estimation theory for causal effects in the presence of network interference. arXiv preprint arXiv:1702.03578 (2017).
  33. Overlapping experiment infrastructure: More, better, faster experimentation. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining. 17–26.
  34. Panos Toulis and Edward Kao. 2013. Estimation of causal peer influence effects. In International conference on machine learning. PMLR, 1489–1497.
  35. Graph cluster randomization: Network exposure to multiple universes. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. 329–337.
  36. Johan Ugander and Hao Yin. 2023. Randomized graph cluster randomization. Journal of Causal Inference 11, 1 (2023), 20220014.
  37. Orthogonal Traffic Assignment in Online Overlapping A/B Tests. Technical Report. EasyChair.
  38. Estimating the total treatment effect in randomized experiments with unknown network structure. Proceedings of the National Academy of Sciences 119, 44 (2022), e2208975119.
  39. Yuan Yuan and Kristen M Altenburger. 2022. A Two-Part Machine Learning Approach to Characterizing Network Interference in A/B Testing.
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