Causal Meta-Mediation Analysis: Inferring Dose-Response Function From Summary Statistics of Many Randomized Experiments
Causal Meta-Mediation Analysis: Inferring Dose-Response Function From Summary Statistics of Many Randomized Experiments
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因果元中介分析：从许多随机实验的汇总统计中推断剂量反应函数
DOI：
10.1145/3394486.3403313
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Liangjie Hong
中科院分区：
文献类型：
--
作者：
Zenan Wang;Xuan Yin;Tianbo Li;Liangjie Hong
在互联网行业，使用离线开发的算法来推动有助于企业成功的在线产品是很常见的。离线开发的算法由离线评估指标指导，这些指标通常不同于在线业务关键绩效指标（kpi）。为了最大化业务kpi，在所有可用的离线评估指标中选择一个北极星是很重要的。通过注意到在线产品可以通过在线评价指标来度量，即离线评价指标的在线对应，我们将问题分解为两部分。由于离线A/B测试文献解决了第一部分：离线评估指标的反事实估计器，其移动方式与在线同行相同，我们将重点放在第二部分：在线评估指标对业务kpi的因果影响。离线评估指标的北极星应该是其在线对应物在业务KPI中带来最显著提升的那颗星。我们将在线评估指标建模为中介，并将其与业务KPI的因果关系形式化为剂量-响应函数（DRF）。我们的新方法，因果元中介分析，利用许多现有随机实验的汇总统计来识别、估计和测试中介DRF。它易于实施和扩展，与中介分析和元分析文献相比具有许多优势。通过对实际数据的仿真和实现，验证了该方法的有效性。
It is common in the internet industry to use offline-developed algorithms to power online products that contribute to the success of a business. Offline-developed algorithms are guided by offline evaluation metrics, which are often different from online business key performance indicators (KPIs). To maximize business KPIs, it is important to pick a north star among all available offline evaluation metrics. By noting that online products can be measured by online evaluation metrics, the online counterparts of offline evaluation metrics, we decompose the problem into two parts. As the offline A/B test literature works out the first part: counterfactual estimators of offline evaluation metrics that move the same way as their online counterparts, we focus on the second part: causal effects of online evaluation metrics on business KPIs. The north star of offline evaluation metrics should be the one whose online counterpart causes the most significant lift in the business KPI. We model the online evaluation metric as a mediator and formalize its causality with the business KPI as dose-response function (DRF). Our novel approach, causal meta-mediation analysis, leverages summary statistics of many existing randomized experiments to identify, estimate, and test the mediator DRF. It is easy to implement and to scale up, and has many advantages over the literature of mediation analysis and meta-analysis. We demonstrate its effectiveness by simulation and implementation on real data.
回复 Imai、Keele、Tingley 和 Yamamoto 关于因果中介分析的评论。
影响因子：
7
作者：
Pearl,Judea
通讯作者：
Pearl,Judea