13 papers
Temporal Dynamics of Development Aid in Africa: Evidence from a Staggered Difference-in-Differences Study of China and World Bank Projects
Mattias Antar, Adel Daoud, Connor T. Jerzak
Subnational studies of aid effectiveness often rely on repeated cross-sections or nighttime lights, making it difficult to separate local treatment effects from baseline difference…
Detecting and Mitigating Treatment Leakage in Text-Based Causal Inference: Distillation and Sensitivity Analysis
Adel Daoud, Richard Johansson, Connor T. Jerzak
Text-based causal inference increasingly employs textual data as proxies for unobserved confounders, yet this approach introduces a previously undertheorized source of bias: treatm…
Beyond Interaction Effects: Two Logics for Studying Population Inequalities
Adel Daoud
When sociologists and other social scientist ask whether the return to college differs by race and gender, they face a choice between two fundamentally different modes of inquiry.…
Remote Auditing: Design-based Tests of Randomization, Selection, and Missingness with Broadly Accessible Satellite Imagery
Connor T. Jerzak, Adel Daoud
Randomized controlled trials (RCTs) are the benchmark for causal inference, yet field implementation can drift from the registered design or, by chance, yield imbalances. We introd…
Chinese vs. World Bank Development Projects: Insights from Earth Observation and Computer Vision on Wealth Gains in Africa, 2002-2013
Adel Daoud, Cindy Conlin, Connor T. Jerzak
Debates about whether development projects improve living conditions persist, partly because observational estimates can be biased by incomplete adjustment and because reliable out…
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…
Platonic Representations for Poverty Mapping: Unified Vision-Language Codes or Agent-Induced Novelty?
Satiyabooshan Murugaboopathy, Connor T. Jerzak, Adel Daoud
We investigate whether socioeconomic indicators, like household wealth, leave recoverable informational imprints in both satellite imagery (capturing features like buildings and ro…
Benchmarking Debiasing Methods for LLM-based Parameter Estimates
Nicolas Audinet de Pieuchon, Adel Daoud, Connor T. Jerzak +2
Large language models (LLMs) offer an inexpensive yet powerful way to annotate text, but are often inconsistent when compared with experts. These errors can bias downstream estimat…
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…
Effect Heterogeneity with Earth Observation in Randomized Controlled Trials: Exploring the Role of Data, Model, and Evaluation Metric Choice
Connor T. Jerzak, Ritwik Vashistha, Adel Daoud
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 re…
A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of Poverty
Kazuki Sakamoto, Connor T. Jerzak, Adel Daoud
Earth observation (EO) data such as satellite imagery can have far-reaching impacts on our understanding of the geography of poverty, especially when coupled with machine learning…
Can Large Language Models (or Humans) Disentangle Text?
Nicolas Audinet de Pieuchon, Adel Daoud, Connor Thomas Jerzak +2
We investigate the potential of large language models (LLMs) to disentangle text variables--to remove the textual traces of an undesired forbidden variable in a task sometimes know…
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…