提出新方法检测连续处理下的工具变量有效性，突破传统限制。
Testability of Instrumental Variables in Additive Nonlinear, Non-Constant Effects Models
- 基于辅助变量独立性检验（AIT），适用于连续或离散处理的非恒定效应模型。
- 在完整条件下，有效工具变量必满足AIT条件；特定假设下该条件充分必要。
- 支持协变量扩展，实验证明在合成与真实数据上均有效，适合因果推断研究者。
本文研究从观测数据中识别工具变量（IV）的可检验性问题。现有方法多限于离散处理或恒定效应场景，而现实中处理常为连续变量（如药物剂量）且效应非恒定。为此，本文提出一种适用于存在未测量混杂因子的加性非线性、非恒定效应模型的辅助变量独立性检验（AIT）条件，以检验候选变量是否为有效工具变量。在完备性假设下，若工具变量有效，则AIT条件成立；进一步证明，在特定附加条件下，AIT条件是检测所有无效工具变量的充要条件。同时将该条件扩展至包含协变量情形，并设计实用测试算法。在合成数据及三个真实世界数据集上的实验表明，所提方法能有效识别工具变量，具有较强实用性。
原文摘要 · Abstract (English)
We address the issue of the testability of instrumental variables derived from observational data. Most existing testable implications are centered on scenarios where the treatment is a discrete variable, e.g., instrumental inequality (Pearl, 1995), or where the effect is assumed to be constant, e.g., instrumental variables condition based on the principle of independent mechanisms (Burauel, 2023). However, treatments can often be continuous variables, such as drug dosages or nutritional content levels, and non-constant effects may occur in many real-world scenarios. In this paper, we consider an additive nonlinear, non-constant effects model with unmeasured confounders, in which treatments can be either discrete or continuous, and propose an Auxiliary-based Independence Test (AIT) condition to test whether a variable is a valid instrument. We first show that, under the completeness condition, if the candidate instrument is valid, then the AIT condition holds. Moreover, we illustrate the implications of the AIT condition and demonstrate that, under certain additional conditions, the AIT condition is necessary and sufficient to detect all invalid IVs. We also extend the AIT condition to include covariates and introduce a practical testing algorithm. Experimental results on both synthetic and three different real-world datasets show the effectiveness of our proposed condition.
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