Observational Data for Heterogeneous Treatment Effects with Application to Recommender Systems

Akos Lada, A. Peysakhovich, Diego Aparicio, Michael Bailey
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引用次数: 13

Abstract

Decision makers in health, public policy, technology, and social science are increasingly interested in going beyond 'one-size-fits-all' policies to personalized ones. Thus, they are faced with the problem of estimating heterogeneous causal effects. Unfortunately, estimating heterogeneous effects from randomized data requires large amounts of statistical power and while observational data is often available in much larger quantities the presence of unobserved confounders can make using estimates derived from it highly suspect. We show that under some assumptions estimated heterogeneous treatment effects from observational data can preserve the rank ordering of the true heterogeneous causal effects. Such an approach is useful when observational data is large, the set of features is high-dimensional, and our priors about feature importance are weak. We probe the effectiveness of our approach in simulations and show a real-world example in a large-scale recommendations problem.
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应用推荐系统的异质治疗效果的观察数据
卫生、公共政策、技术和社会科学领域的决策者越来越有兴趣从“一刀切”的政策转向个性化的政策。因此,他们面临着估计异质性因果效应的问题。不幸的是,从随机数据中估计异质效应需要大量的统计能力,虽然观测数据的数量往往要大得多,但未观察到的混杂因素的存在会使使用由此得出的估计变得非常可疑。我们表明,在某些假设下,从观测数据估计的异质治疗效应可以保持真正的异质因果效应的秩顺序。这种方法适用于观测数据量大、特征集高维、特征重要性先验较弱的情况。我们在模拟中探索了我们的方法的有效性,并在一个大规模推荐问题中展示了一个现实世界的例子。
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