Effect Heterogeneity with Earth Observation in Randomized Controlled Trials: Exploring the Role of Data, Model, and Evaluation Metric Choice
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随机对照试验中的地球观测与效应异质性：数据、模型及评价指标的选择
- 研究问题
- 如何利用时间序列卫星影像分析随机对照试验的条件平均治疗效应，并评估数据来源、模型和评价指标的影响？
- 方法与思路
- 作者比较卫星影像序列的不同表征模型，开展模拟及两项试验应用，并以地表覆盖分类解释异质性的影像特征。
- 材料陈述的贡献
- 摘要称参数更多的序列表征模型通常更能检出异质性，并提供影像、表格及组合数据比较和试验外地理推广的证据。
- 阅读关注点
- 应核对影像采集时间、效应评价指标、模型比较设计，以及向试验外地区推广所需条件与地表覆盖解释的依据。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未说明推广的识别条件，模型参数量结论也仅来自所述研究。
原文摘要
Many social and environmental phenomena are associated with macroscopic changes in the built environment, captured by satellite imagery on a global scale and with daily temporal resolution. While widely used for prediction, these images and especially image sequences remain underutilized for causal inference, especially in the context of randomized controlled trials (RCTs), where causal identification is established by design. In this paper, we develop and compare a set of general tools for analyzing Conditional Average Treatment Effects (CATEs) from temporal satellite data that can be applied to any RCT where geographical identifiers are available. Through a simulation study, we analyze different modeling strategies for estimating CATE in sequences of satellite images. We find that image sequence representation models with more parameters generally yield a greater ability to detect heterogeneity. To explore the role of model and data choice in practice, we apply the approaches to two influential RCTs -- Banerjee et al. (2015), a poverty study in Cusco, Peru, and Bolsen et al. (2014), a water conservation experiment in Georgia, USA. We benchmark our image sequence models against image-only, tabular-only, and combined image-tabular data sources, summarizing practical implications for investigators in a multivariate analysis. Land cover classifications over satellite images facilitate interpretation of what image features drive heterogeneity. We also show robustness to data and model choice of satellite-based generalization of the RCT results to larger geographical areas outside the original. Overall, this paper shows how satellite sequence data can be incorporated into the analysis of RCTs, and provides evidence about the implications of data, model, and evaluation metric choice for causal analysis.