Summary of A Scoping Review Of Earth Observation and Machine Learning For Causal Inference: Implications For the Geography Of Poverty, by Kazuki Sakamoto et al.
A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of Poverty
by Kazuki Sakamoto, Connor T. Jerzak, Adel Daoud
First submitted to arxiv on: 30 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Methodology (stat.ME); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A recent surge in using Earth observation (EO) data, such as satellite imagery, in conjunction with machine learning (ML) and computer vision has significant implications for understanding geography of poverty. Early research focused on predictive models estimating living conditions in areas with limited data availability. Building upon this work, researchers have now applied EO data to conduct causal inference, but the methods and applications remain largely unexplored. This study conducts a scoping review to document the growth of interest in using satellite images and other sources of EO data in causal analysis, tracing the methodological relationship between spatial statistics and ML methods. The paper highlights five ways EO data has been used in scientific workflows: outcome imputation, EO image deconfounding, treatment effect heterogeneity, transportability analysis, and image-informed causal discovery. By providing a detailed workflow for incorporating EO data in causal analysis, this research enables researchers to design more effective studies, from data requirements to choice of computer vision model and evaluation metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary EO data can help us understand poverty and improve living conditions around the world. Scientists are using machine learning and computer vision to analyze satellite images and make predictions about where people live and how they are doing. This research looks at how scientists are using these techniques to figure out why things happen, rather than just predicting what will happen. The study found that there are many different ways that EO data can be used in scientific studies, including imputing missing data, removing bias from images, and analyzing the effects of different factors on living conditions. By providing a step-by-step guide on how to use EO data in these types of studies, this research aims to help scientists design better studies and make more accurate predictions. |
Keywords
» Artificial intelligence » Inference » Machine learning
