  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.
