5 citations · 15 across the 24 of their papers we have counts for
9 papers · 1 filter
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…
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…
Correcting Bias When Using Latent Regressors
Connor T. Jerzak, Stephen A. Jessee
Many core concepts in political science are latent and therefore can only be measured with error. Measurement error in a predictor attenuates slope coefficient estimates in regress…
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…
MiniMax Learning of Interpretable Factored Stochastic Policies from Conjoint Data, with Uncertainty Quantification
Connor T. Jerzak, Priyanshi Chandra, Rishi Hazra
We study offline policy optimization over exponentially large factorial action spaces from randomized preference data, showing how conjoint experiments can estimate interpretable s…
FastRerandomize: Fast Rerandomization Using Accelerated Computing
Rebecca Goldstein, Connor T. Jerzak, Aniket Kamat +1
We present fastrerandomize, an R package for fast, scalable rerandomization in experimental design. Rerandomization improves precision by discarding treatment assignments that fail…