Recent developments in deep representation models through counterfactual balancing have led to a promising framework for estimating Individual Treatment Effects (ITEs) that are essential to causal inference in the Neyman-Rubin potential outcomes framework. While Randomized Control Trials are vital to understanding causal effects, they are sometimes infeasible, costly, or unethical to conduct. Motivated by these potential obstacles to data acquisition, we focus on transferring the causal knowledge acquired in prior experiments to new scenarios for which only limited data is available. To this end, we first observe that the absolute values of ITEs are invariant under the action of the symmetric group on the labels of treatments. Given this invariance, we propose a symmetrized task distance for calculating the similarity of a target scenario with those encountered before. The aforementioned task distance is then used to transfer causal knowledge from the closest of all the available previously learned tasks to the target scenario. We provide upper bounds on the counterfactual loss and ITE error of the target task indicating the transferability of causal knowledge. Empirical studies are provided for various real-world, semi-synthetic, and synthetic datasets demonstrating that the proposed symmetrized task distance is strongly related to the estimation of the counterfactual loss. Numerical results indicate that transferring causal knowledge reduces the amount of required data by up to 95% when compared to training from scratch. These results reveal the promise of our method when applied to important albeit challenging real-world scenarios such as transferring the knowledge of treatment effects (e.g., medicine, social policy, personal training, etc.) studied on a population to other groups absent in the study.
翻译:通过反事实平衡实现的深层代表性模型的近期发展导致了一个很有希望的估算个人治疗效果的框架(ITE),这是Neyman-Rubin潜在结果框架中因果推断的关键。尽管随机化控制试验对于理解因果关系至关重要,但有时不可行、费用昂贵或不道德地进行。由于在获取数据方面存在着这些潜在的障碍,我们侧重于将先前实验中获得的因果关系知识转移到只有有限数据的新假设中。为此,我们首先观察到,在治疗标签的对称小组的行动下,ITE的绝对值是变化性的。鉴于这种差异性,我们提议在计算目标情景与以前遇到的情况相似性时,随机化控制试验是一个对等的任务距离。上述任务距离被用来将因果关系知识从最接近以前所学到的所有任务转移到目标情景中。我们提供了反事实性损失和ITE目标错误的上限,表明因果关系知识的可转移性。对于各种现实-世界的对结果进行了实证性研究,在进行对比性研究时,半级合成同步数据分析结果显示,这些对真实性数据的分析结果进行了精确性分析后,这些对真实性分析结果进行了定量分析后,将分析后,将分析后,将分析后将分析结果的数值分析结果将显示为真实性分析结果的数值分析结果,将降低。