The choice of making an intervention depends on its potential benefit or harm
in comparison to alternatives. Estimating the likely outcome of alternatives
from observational data is a challenging problem as all outcomes are never
observed, and selection bias precludes the direct comparison of differently
intervened groups. Despite their empirical success, we show that algorithms
that learn domain-invariant representations of inputs (on which to make
predictions) are often inappropriate, and develop generalization bounds that
demonstrate the dependence on domain overlap and highlight the need for
invertible latent maps. Based on these results, we develop a deep kernel
regression algorithm and posterior regularization framework that substantially
outperforms the state-of-the-art on a variety of benchmarks data sets.

选择干预的决策取决于其潜在的利益或伤害与替代方案相比。通过观察性数据估计替代方案的可能结果是一个具有挑战性的问题，尽管深度核回归算法和后验正则化框架在多个基准数据集上表现优越，但我们发现学习输入的域不变表示的算法经常不合适，并开发以依赖于域重叠和强调需要可逆潜在映射的泛化界限。