Publications (34)
Navigating Unmeasured Confounding in Quantitative Sociology: A Sensitivity Framework
Cheng Lin, Jose M. Pena, Adel Daoud
Unmeasured confounding remains a critical challenge in causal inference for the social sciences. This paper proposes a sensitivity analysis framework to systematically evaluate how…
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…
Sensitivity Analysis to Unobserved Confounding with Copula-based Normalizing Flows
Sourabh Balgi, Marc Braun, Jose M. Peña +1
We propose a novel method for sensitivity analysis to unobserved confounding in causal inference. The method builds on a copula-based causal graphical normalizing flow that we term…
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…
A High Resolution Urban and Rural Settlement Map of Africa Using Deep Learning and Satellite Imagery
Mohammad Kakooei, James Bailie, Markus B. Pettersson +3
Accurate and consistent mapping of urban and rural areas is crucial for sustainable development, spatial planning, and policy design. It is particularly important in simulating the…
CausalImages: An R Package for Causal Inference with Earth Observation, Bio-medical, and Social Science Images
Connor T. Jerzak, Adel Daoud
The causalimages R package enables causal inference with image and image sequence data, providing new tools for integrating novel data sources like satellite and bio-medical imager…