Chasing Shadows: How Implausible Assumptions Skew Our Understanding of Causal Estimands

Stijn Vansteelandt, Kelly Van Lancker
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Abstract

The ICH E9 (R1) addendum on estimands, coupled with recent advancements in causal inference, has prompted a shift towards using model-free treatment effect estimands that are more closely aligned with the underlying scientific question. This represents a departure from traditional, model-dependent approaches where the statistical model often overshadows the inquiry itself. While this shift is a positive development, it has unintentionally led to the prioritization of an estimand's theoretical appeal over its practical learnability from data under plausible assumptions. We illustrate this by scrutinizing assumptions in the recent clinical trials literature on principal stratum estimands, demonstrating that some popular assumptions are not only implausible but often inevitably violated. We advocate for a more balanced approach to estimand formulation, one that carefully considers both the scientific relevance and the practical feasibility of estimation under realistic conditions.
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追逐阴影:匪夷所思的假设如何歪曲我们对因果估计的理解
ICH E9 (R1)关于估计值的附录,加上最近因果推断方面的进步,促使人们转向使用与基本科学问题更密切相关的无模型治疗效果估计值。虽然这种转变是一种积极的发展,但它无意中导致了估算指标的理论吸引力优先于其在合理假设下从数据中的实际可学习性。我们通过对近期临床试验文献中有关本底估计值的假设进行细分来说明这一点,证明一些流行的假设不仅不合理,而且经常不可避免地遭到违反。我们主张采用更加平衡的方法来制定估计值,即在现实条件下仔细考虑估计值的科学相关性和实际可行性。
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