推荐系统中隐变量干扰普遍存在，影响效果评估与优化。
Confounding is a Pervasive Problem in Real World Recommender Systems
- 特征工程、A/B测试等常见做法会引入隐藏干扰
- 实验证明这些干扰导致推荐效果估计偏差
- 适合算法工程师与数据科学实践者阅读
未观测到的混淆因素同时影响处理和结果时，会导致因果效应估计偏差。这一问题在经济学、医学等领域广泛存在。尽管推荐系统使用完全可观测数据，看似不受此影响，但实际中特征工程、A/B测试和模块化等常见做法会忽略部分可观测特征，从而产生等效的混淆问题。本文揭示了多项标准实践可能引入混淆，并通过模拟实验验证其影响，提出具体建议以帮助从业者减少或避免此类干扰对真实系统性能的损害。
原文摘要 · Abstract (English)
Unobserved confounding arises when an unmeasured feature influences both the treatment and the outcome, leading to biased causal effect estimates. This issue undermines observational studies in fields like economics, medicine, ecology or epidemiology. Recommender systems leveraging fully observed data seem not to be vulnerable to this problem. However many standard practices in recommender systems result in observed features being ignored, resulting in effectively the same problem. This paper will show that numerous common practices such as feature engineering, A/B testing and modularization can in fact introduce confounding into recommendation systems and hamper their performance. Several illustrations of the phenomena are provided, supported by simulation studies with practical suggestions about how practitioners may reduce or avoid the affects of confounding in real systems.
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