论文标题
积极性：可识别性和估计性
Positivity: Identifiability and Estimability
论文作者
论文摘要
积极的假设是，在人群中发生的混杂变量的每种独特组合都具有非零的作用概率，可以进一步描述为确定性的积极性和随机阳性。在这里，我们重新审视了这种区别，研究了其与非参数可识别性和估计性的关系，并讨论如何解决违反阳性假设的行为。最后，我们将积极性与最近对机器学习的兴趣以及因果推断的数据自适应算法的局限性联系起来。积极性通常可能会被忽略，但对于推断仍然很重要。
Positivity, the assumption that every unique combination of confounding variables that occurs in a population has a non-zero probability of an action, can be further delineated as deterministic positivity and stochastic positivity. Here, we revisit this distinction, examine its relation to nonparametric identifiability and estimability, and discuss how to address violations of positivity assumptions. Finally, we relate positivity to recent interest in machine learning, as well as the limitations of data-adaptive algorithms for causal inference. Positivity may often be overlooked, but it remains important for inference.
