Estimating Causal Effects from Data Generated by Stochastic Algorithms
利用随机算法生成的数据估计因果效应AI 解读 ↓
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利用随机算法生成的数据估计因果效应
- 研究问题
- 推荐或生成内容同时受用户特征与多维内容差异影响时，如何利用曝光算法中的随机性识别内容特征对用户结果的因果作用？
- 方法与思路
- 作者要求记录实际曝光项、至少一个未曝光候选项及两者曝光概率比估计，利用随机曝光机制构建内容特征效应估计器。
- 材料陈述的贡献
- 摘要称在该数据结构下，即使未观测因素同时影响用户偏好及候选配对，也能识别特征效应，并提出区别于既有方法的估计器。
- 阅读关注点
- 应核对未曝光候选项的生成方式、概率比的定义与估计误差，以及识别的特征效应目标和允许未观测混杂的具体范围。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收均未核验；摘要未列出完整识别条件，不能认为任意随机推荐日志均可识别特征效应。
原文摘要
Recommendation systems and chatbots present content to users, typically using stochastic algorithms that select the content based on user characteristics or context. Examples of content include chat responses, videos, or items available for purchase. Scientists and application developers are often interested in whether characteristics of content increase outcomes such as user engagement. Estimates of such causal effects may guide content providers to generate content that emphasize desirable features. However, in settings with a large content library or where content is generated uniquely for a given user, it can be difficult to use observational data to learn the causal effect of content features, because the content a user sees is tailored to that user, and because content varies in many dimensions. This paper proposes a new method for estimating the impact of content features using observational data, when the algorithm that determines user exposure incorporates some randomization, and when two additional data elements are logged for each user: $(i)$ the identity of at least one item that could have been exposed to the user, but was not (the unexposed item); $(ii)$ an estimate of the ratio of the probability that the unexposed item would have been shown to the probability that the exposed item was shown. We show that causal effects of features are identified in this setting, even in the presence of unobserved confounders that affect both user preferences and the identity of the considered pair of items (exposed and unexposed). Our estimator differs from prior approaches in terms of what data is used and how the estimator is constructed.