Transporting causal effects from a randomized trial without "transportability:" a case study of political advertising during U.S. elections
放宽可迁移性假设的随机试验效应外推：美国选举政治广告案例AI 解读 ↓
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放宽可迁移性假设的随机试验效应外推：美国选举政治广告案例
- 研究问题
- 源总体随机试验中的处理效应如何外推至背景不同的目标总体，尤其当观测变量不能解释全部总体差异、协变量还不一致时？
- 方法与思路
- 作者以敏感性分析容许可迁移性违背，提出带 Bootstrap 的回归估计器和有效影响函数估计器，并用样本拆分校准敏感性参数。
- 材料陈述的贡献
- 摘要称，框架允许源与目标协变量不同；佐治亚州广告应用显示，小幅可迁移性偏离即可对应明显不同的县级投票率效应。
- 阅读关注点
- 应核对敏感性参数如何限制未观测总体差异、不同协变量如何衔接，以及样本拆分校准和目标效应推断的识别条件。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未给出敏感性模型和效应数值，放宽可迁移性仍需其他外推限制。
原文摘要
During the 2020 U.S. presidential election, Aggarwal et al. (2023) conducted a large-scale randomized experiment to evaluate a digital ad campaign against Trump in five battleground states. While the study found no effect on voter turnout, it's unclear whether this null result generalizes to other battleground states, notably Georgia, which played a unique role in the 2020 election and differs from the battleground states. Inspired by the study, we present a transfer learning framework to estimate treatment effects in a target population (e.g., Georgia) based on a randomized experiment from a source population (e.g., the five battleground states). Our framework is based on a sensitivity analysis that allows for violation of transportability, a popular yet impractical assumption which requires all differences between the source and target populations to be characterized by observed variables. Under our framework, we propose two estimators of the target treatment effect: a simple regression estimator with bootstrap, which we recommend for practitioners in this field, and an estimator based on the efficient influence function. Importantly, both estimators allow for covariates to differ between the target and the source populations, another common scenario in practice. We also propose a new, sample splitting approach to calibrate the sensitivity parameter. We apply our framework to estimate the effect of the ad campaign on voter turnout in Georgia during the 2020 election. Our findings indicate that small departures from transportability can lead to dramatically different ad effects across counties of Georgia. The direction of the effects is largely driven by racial composition: counties with higher White and lower Black percents tend to show positive effects, while counties with higher Latinx percents tend to show negative effects.