Quantifying causal effects in the presence of complex and multivariate outcomes remains a key challenge in treatment evaluation. For hierarchical multivariate outcomes, the FDA recommends the Win Ratio and Generalized Pairwise Comparisons approaches \citep{Pocock2011winratio,Buyse2010}. However, commonly used estimators can yield treatment recommendations that target a population-level estimand (the probability that a randomly sampled patient under treatment fares better than another randomly sampled patient under control), which can contradict conclusions drawn from an ideal estimand (the probability that an individual would fare better with treatment than without), especially in heterogeneous populations. This discrepancy arises from the non-identifiability of the latter estimand and underscores both the influence of the chosen causal measure on the resulting conclusions and the necessity of articulating the underlying causal framework with clarity. We propose a novel, individual-level yet identifiable causal effect measure that more closely approximates the ideal individual-level estimand. We show that computing the Win Ratio or Net Benefit via nearest-neighbor pairing between treated and control patients, which can be seen as an extreme form of stratification, yields an estimator of our new causal measure in both randomized controlled trials and observational settings. We then develop a distributional regression framework, alongside semiparametric efficient estimators. Our methods are simple to implement and readily applicable in practice. We evaluate the proposed approach through simulations and apply it to the CRASH-3 trial \citep{crash3}, a major study assessing the effects of tranexamic acid in patients with traumatic brain injury.

翻译：在存在复杂且多变量结局的情况下，量化因果效应仍是治疗评估中的关键挑战。对于分层多变量结局，FDA推荐使用胜率比和广义配对比较方法 \citep{Pocock2011winratio,Buyse2010}。然而，常用的估计量可能产生针对群体水平估计值（即随机选取的一名治疗组患者比另一名随机选取的对照组患者表现更佳的概率）的治疗建议，这可能与从理想估计值（即一名个体接受治疗比不接受治疗表现更佳的概率）中得出的结论相矛盾，尤其在异质性群体中。这种差异源于后者估计值的不可识别性，既突显了所选因果度量对最终结论的影响，也强调了阐明潜在因果框架的必要性。我们提出了一种新颖的、个体水平但可识别的因果效应度量，它能更接近地逼近理想的个体水平估计值。我们证明，通过治疗组与对照组患者之间最近邻配对（可视为一种极端分层形式）计算胜率比或净获益，在随机对照试验和观察性研究中均能得出新因果度量的估计值。随后，我们开发了一个分布回归框架，以及半参数有效估计量。我们的方法易于实现，可在实践中直接应用。我们通过模拟评估了所提出的方法，并将其应用于CRASH-3试验 \citep{crash3}——一项评估氨甲环酸对创伤性脑损伤患者效果的重要研究。