5 citations · 15 across the 24 of their papers we have counts for
3 papers · 1 filter
Debiasing Machine Learning Predictions for Causal Inference Without Additional Ground Truth Data: "One Map, Many Trials" in Satellite-Driven Poverty Analysis
Markus B. Pettersson, Connor T. Jerzak, Adel Daoud
Machine learning models trained on Earth observation data, such as satellite imagery, have demonstrated significant promise in predicting household-level wealth indices, enabling t…
Optimizing Multi-Scale Representations to Detect Effect Heterogeneity Using Earth Observation and Computer Vision: Applications to Two Anti-Poverty RCTs
Fucheng Warren Zhu, Connor T. Jerzak, Adel Daoud
Earth Observation (EO) data are increasingly used in policy analysis by enabling granular estimation of conditional average treatment effects (CATE). However, a challenge in EO-bas…
Integrating Earth Observation Data into Causal Inference: Challenges and Opportunities
Connor T. Jerzak, Fredrik Johansson, Adel Daoud
Observational studies require adjustment for confounding factors that are correlated with both the treatment and outcome. In the setting where the observed variables are tabular qu…