Design-based theory for causal inference from adaptive experiments
自适应实验因果推断的设计本位理论AI 解读 ↓
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自适应实验因果推断的设计本位理论
- 研究问题
- 当实验依据历史信息更新处理概率，且单位可能不可交换、随时间变化时，如何在有限总体框架中进行有效因果推断？
- 方法与思路
- 作者建立一般自适应设计下 IPW 与 AIPW 的设计本位理论，提出协方差估计，并利用自适应协变量调整形成鞅结构。
- 材料陈述的贡献
- 摘要称理论容纳不收敛或趋零的处理概率及不收敛的结果估计器，并为黑箱模型调整和多类自适应随机设计提供推断依据。
- 阅读关注点
- 应核对处理概率趋零时仍需满足的条件、协方差估计何时达到锐界，以及自适应调整如何支持黑箱结果模型的有效推断。
- 阅读边界
- 这是 arXiv 预印本，不能据此确认同行评审或期刊接收；摘要未说明完整正则条件，不能据此认为任意自适应设计均有效。
原文摘要
Adaptive designs dynamically update treatment probabilities using information accumulated during the experiment. Existing theory for causal inference from adaptive experiments primarily assumes the superpopulation framework with independent and identically distributed units, and may not apply when the distribution of units evolves over time. This paper makes two contributions. First, we extend the literature to the finite-population framework, which allows for possibly nonexchangeable units, and establish the design-based theory for causal inference under general adaptive designs using inverse-propensity-weighted (IPW) and augmented IPW (AIPW) estimators. Our theory accommodates nonexchangeable units, both nonconverging and vanishing treatment probabilities, and nonconverging outcome estimators, thereby justifying inference using AIPW estimators with black-box outcome models that integrate advances from machine learning methods. To alleviate the conservativeness inherent in variance estimation under finite-population inference, we also introduce a covariance estimator for the AIPW estimator that becomes sharp when the residuals from the adaptive regression of potential outcomes on covariates are additive across units. Our framework encompasses widely used adaptive designs, such as multi-armed bandits, covariate-adaptive randomization, and sequential rerandomization, advancing the design-based theory for causal inference in these specific settings. Second, as a methodological contribution, we propose an adaptive covariate adjustment approach for analyzing even nonadaptive designs. The martingale structure induced by adaptive adjustment enables valid inference with black-box outcome estimators that would otherwise require strong assumptions under standard nonadaptive analysis.