Removing Hidden Confounding by Experimental Grounding
Removing Hidden Confounding by Experimental Grounding
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发表时间：
2018-10
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通讯作者：
Nathan Kallus;A. Puli;Uri Shalit
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作者：
Nathan Kallus;A. Puli;Uri Shalit
观察数据越来越多地被用作进行个人层面因果预测和干预建议的手段。从观测数据中进行因果推断的最大挑战是隐藏的混杂，其存在无法在数据中进行测试，并且可以使任何因果结论无效。实验数据不受混杂因素的影响，但通常在范围和规模上都受到限制。我们介绍了一种新的方法，使用有限的实验数据来纠正在较大的观测数据上训练的因果效应模型中隐藏的混淆，即使观测数据与实验数据不完全重叠。我们的方法比现有的方法严格弱的假设，我们证明了条件下，它产生一个一致的估计。我们使用来自大型教育实验的真实数据证明了我们方法的有效性。
Observational data is increasingly used as a means for making individual-level causal predictions and intervention recommendations. The foremost challenge of causal inference from observational data is hidden confounding, whose presence cannot be tested in data and can invalidate any causal conclusion. Experimental data does not suffer from confounding but is usually limited in both scope and scale. We introduce a novel method of using limited experimental data to correct the hidden confounding in causal effect models trained on larger observational data, even if the observational data does not fully overlap with the experimental data. Our method makes strictly weaker assumptions than existing approaches, and we prove conditions under which it yields a consistent estimator. We demonstrate our method's efficacy using real-world data from a large educational experiment.