The Stable Unit Treatment Value Assumption (SUTVA) includes the condition that there are no multiple versions of treatment in causal inference. Though we could not control the implementation of treatment in observational studies, multiple versions may exist in the treatment. It has been pointed out that ignoring such multiple versions of treatment can lead to biased estimates of causal effects, but a causal inference framework that explicitly deals with the unbiased identification and estimation of version-specific causal effects has not been fully developed yet. Thus, obtaining a deeper understanding for mechanisms of the complex treatments is difficult. In this paper, we introduce the Mixture-of-Experts framework into causal inference and develop a methodology for estimating the causal effects of latent versions. This approach enables explicit estimation of version-specific causal effects even if the versions are not observed. Numerical experiments demonstrate the effectiveness of the proposed method.

翻译：在因果推断中，稳定单位处理值假设（SUTVA）包含不存在多重治疗版本的条件。然而在观察性研究中，尽管我们无法控制治疗的实施方式，治疗本身可能存在多种版本。已有研究指出，忽略此类多重治疗版本会导致因果效应的估计产生偏差，但一个能够明确处理版本特异性因果效应的无偏识别与估计的因果推断框架尚未完全建立。因此，难以深入理解复杂治疗的作用机制。本文在因果推断中引入混合专家框架，并开发了一种用于估计潜在版本因果效应的方法。该方法即使在各版本未被观测的情况下，也能显式估计版本特异性因果效应。数值实验验证了所提方法的有效性。