The notion of exchangeability has been recognized in the causal inference literature in various guises, but only rarely in the original Bayesian meaning as a symmetry property between individual units in statistical inference. Since the latter is a standard ingredient in Bayesian inference, we argue that in Bayesian causal inference it is natural to link the causal model, including the notion of confounding and definition of causal contrasts of interest, to the concept of exchangeability. Here we relate the Bayesian notion of exchangeability to alternative conditions for unconfounded inferences, commonly stated using potential outcomes, and define causal contrasts in the presence of exchangeability in terms of limits of posterior predictive expectations for further exchangeable units. While our main focus is in a point treatment setting, we also investigate how this reasoning carries over to longitudinal settings.

翻译：在因果推断文献中,各种伪装的因果推断文献承认了可交换性概念,但在贝叶斯的原始含义中,作为统计推论中个别单位之间的对称性属性,这种可交换性概念很少被确认为统计推论中个别单位之间的对称性属性,由于后者是贝叶斯推论中的标准成份,我们争辩说,在巴伊斯的因果推论中,将因果模型,包括因果差异概念和定义,与可交换性概念联系起来是自然的。 我们在这里将贝叶斯的可交换性概念与无根据推论的替代条件联系起来,通常使用潜在结果加以说明,并界定在对进一步可交换的单位的后继预测预期的限度方面存在着可交换性的因果差异。我们的主要重点是点处理环境,我们还调查这一推论如何延续到长相环境。