Evaluating the incremental return on ad spend (iROAS) of a prospective online marketing strategy (i.e., the ratio of the strategy's causal effect on some response metric of interest relative to its causal effect on the ad spend) has become increasingly more important. Although randomized ``geo experiments'' are frequently employed for this evaluation, obtaining reliable estimates of iROAS can be challenging as oftentimes only a small number of highly heterogeneous units are used. Moreover, advertisers frequently impose budget constraints on their ad spends, which further complicates causal inference by introducing interference between the experimental units. In this paper, we formulate a novel statistical framework for inferring the iROAS of online advertising from randomized paired geo experiment which further motivates and provides new insights into Rosenbaum's arguments on instrumental variables, and we propose and develop a robust, distribution-free and interpretable estimator ``Trimmed Match'', as well as a data-driven choice of the tuning parameter which may be of independent interest. We investigate the sensitivity of Trimmed Match to some violations of its assumptions and show that it can be more efficient than some alternative estimators based on simulated data. We then demonstrate its practical utility with real case studies.
翻译:评估预期在线营销战略(即战略对某种响应指标的因果效应相对于其对支出的因果关系的影响比率)的递增回报率(iROAS),已经变得越来越越来越重要了。虽然经常使用随机的“geo实验”来进行评估,但获取对iROAS的可靠估计可能具有挑战性,因为通常只使用数量众多的高度多样化单位,因此通常只能使用对iROAS的可靠估计数,因为通常只使用少数高度多样化单位。此外,广告商经常对其广告支出施加预算限制,这通过在实验单位之间引入干扰,使因果关系推断进一步复杂化。在本文中,我们制定了一个新的统计框架,用以从随机化配对配对的地球实验中推断网上广告的iROAS从随机化的在线广告与对花支出的因果关系度指标中推断出iROAS,这种随机化的配对配对配对地球实验将进一步激励和提供对罗森马关于工具变量论点的新见解,并提供了新的见解,我们提议和开发一个强大、无分发和可解释的、不易分发、不易分发、不易分发和可解释、可解释的估量的估量的系统,以及数据选择可能独立感兴趣的参数的选择选择。我们根据数据,我们根据一些替代数据,根据其他数据,根据数据,根据数据,对调调调调调参数,根据数据对调参数,调查对点的敏感度对一些假设的敏感性,我们调查,对一些假设的比较的敏感度研究,我们调查,显示其实际研究,可以更模拟比模拟研究,我们案的比其他数据,更能性研究,我们测点可以更能,用,用,用,我们测,我们测,我们测,更能,我们测,更能更能更能,更能,我们测,我们用,我们用,用。