← Search

Connor Thomas Jerzak

2 accepted papers

2026

Debiasing Machine Learning Predictions for Causal Inference Without Additional Ground Truth Data: “One Map, Many Trials” in Satellite-Driven Poverty Analysis

AAAI 2026technical

Machine learning models trained on Earth observation data, such as satellite imagery, have demonstrated significant promise in predicting household-level wealth indices, enabling the creation of high-resolution wealth maps that can be leveraged across multiple causal trials while addressing chronic

Cited by 4SourcePDFScholar
2025

Benchmarking Debiasing Methods for LLM-based Parameter Estimates

EMNLP 2025

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 estimates of population parameters such as regression coefficients and causal effects. To mitigate this bias, researchers have

Cited by 0SourcePDFScholar