We consider the problem of designing a randomized experiment on a source population to estimate the Average Treatment Effect (ATE) on a target population. We propose a novel approach which explicitly considers the target when designing the experiment on the source. Under the covariate shift assumption, we design an unbiased importance-weighted estimator for the target population's ATE. To reduce the variance of our estimator, we design a covariate balance condition (Target Balance) between the treatment and control groups based on the target population. We show that Target Balance achieves a higher variance reduction asymptotically than methods that do not consider the target population during the design phase. Our experiments illustrate that Target Balance reduces the variance even for small sample sizes.
翻译:我们考虑对源人口设计随机实验的问题,以估计对目标人口的平均治疗效果。我们建议一种新颖的方法,在设计源实验时明确考虑目标。根据共变转换假设,我们为目标人群的ATE设计了一个不带偏见的重要性加权估计计算器。为了缩小我们估算器的差异,我们设计了一个基于目标人群的治疗和控制组之间的混合平衡条件(目标平衡)。我们表明,目标平衡比在设计阶段不考虑目标人群的方法在不考虑目标人群的情况下实现了更高的零星差异减少。我们的实验表明,即使对于小样本规模,目标平衡也降低了差异。