تقرير
Combining Rollout Designs and Clustering for Causal Inference under Low-order Interference
| العنوان: | Combining Rollout Designs and Clustering for Causal Inference under Low-order Interference |
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| المؤلفون: | Cortez-Rodriguez, Mayleen, Eichhorn, Matthew, Yu, Christina Lee |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Statistics - Methodology, Computer Science - Social and Information Networks, 62K99 (Primary), 62P30 (Secondary) |
| الوصف: | Estimating causal effects under interference is pertinent to many real-world settings. However, the true interference network may be unknown to the practitioner, precluding many existing techniques that leverage this information. A recent line of work with low-order potential outcomes models uses staggered rollout designs to obtain unbiased estimators that require no network information. However, their use of polynomial extrapolation can lead to prohibitively high variance. To address this, we propose a two-stage experimental design that restricts treatment rollout to a sub-population. We analyze the bias and variance of an interpolation-style estimator under this experimental design. Through numerical simulations, we explore the trade-off between the error attributable to the subsampling of our experimental design and the extrapolation of the estimator. Under low-order interactions models with degree greater than 1, the proposed design greatly reduces the error of the polynomial interpolation estimator, such that it outperforms baseline estimators, especially when the treatment probability is small. Comment: 30 pages, 13 figures |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2405.05119 |
| رقم الأكسشن: | edsarx.2405.05119 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |