Instrumental variable methods provide a powerful approach to estimating
causal effects in the presence of unobserved confounding. But a key challenge
when applying them is the reliance on untestable "exclusion" assumptions that
rule out any relationship between the instrument variable and the response that
is not mediated by the treatment. In this paper, we show how to perform
consistent IV estimation despite violations of the exclusion assumption. In
particular, we show that when one has multiple candidate instruments, only a
majority of these candidates---or, more generally, the modal candidate-response
relationship---needs to be valid to estimate the causal effect. Our approach
uses an estimate of the modal prediction from an ensemble of instrumental
variable estimators. The technique is simple to apply and is "black-box" in the
sense that it may be used with any instrumental variable estimator as long as
the treatment effect is identified for each valid instrument independently. As
such, it is compatible with recent machine-learning based estimators that allow
for the estimation of conditional average treatment effects (CATE) on complex,
high dimensional data. Experimentally, we achieve accurate estimates of
conditional average treatment effects using an ensemble of deep network-based
estimators, including on a challenging simulated Mendelian Randomization
problem.

本文提出一种方法，使用多个工具变量估计器计算最可能的因果效应，从而在不依赖于「排除假设」的情况下进行一致的工具变量估计。