Typical causal effects are defined based on the marginal distribution of potential outcomes. However, many real-world applications require causal estimands involving the joint distribution of potential outcomes to enable more nuanced treatment evaluation and selection. In this article, we propose a novel framework for identifying and estimating the joint distribution of potential outcomes using multiple experimental datasets. We introduce the assumption of transportability of state transition probabilities for potential outcomes across datasets and establish the identification of the joint distribution under this assumption, along with a regular full-column rank condition. The key identification assumptions are testable in an overidentified setting and are analogous to those in the context of instrumental variables, with the dataset indicator serving as "instrument". Moreover, we propose an easy-to-use least-squares-based estimator for the joint distribution of potential outcomes in each dataset, proving its consistency and asymptotic normality. We further extend the proposed framework to identify and estimate principal causal effects. We empirically demonstrate the proposed framework by conducting extensive simulations and applying it to evaluate the surrogate endpoint in a real-world application.

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