5 citations · 15 across the 24 of their papers we have counts for
25 papers
ExpertMuon-Compass: Alignment-Guided Step Sizes for Mixture-of-Experts Training
Omatharv Bharat Vaidya, Ashwin Vinod, Pedram Akbarian +3
Mixture-of-experts (MoE) language models send each token to a few experts, so each expert is trained on a different part of the data, and this part changes during training. With a…
When Many Answers Are Valid, Voting Fails: Symbolic Verification for Best-of-K Causal Reasoning in LLMs
Omatharv Bharat Vaidya, Connor Thomas Jerzak, Zayne Rea Sprague +2
Self-consistency assumes the most frequent answer among sampled reasoning traces is the most reliable, but this can fail in causal reasoning: samples often repeat the same confound…
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…
Queryable LoRA: Instruction-Regularized Routing Over Shared Low-Rank Update Atoms
Omatharv Bharat Vaidya, Connor T. Jerzak, Nhat Ho +1
We present a data-adaptive method for parameter-efficient fine-tuning of large neural networks. Standard low-rank adaptation methods improve efficiency by restricting each layer up…
Multiplication in Multimodal LLMs: Computation with Text, Image, and Audio Inputs
Samuel G. Balter, Ethan Jerzak, Connor T. Jerzak
Multimodal LLMs can accurately perceive numerical content across modalities yet fail to perform exact multi-digit multiplication when the identical underlying arithmetic problem is…
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…
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…
Degrees of Randomness in Rerandomization Procedures
Connor T. Jerzak, Rebecca Goldstein
Randomized controlled trials are susceptible to imbalance on covariates predictive of the outcome. Rerandomization and deterministic treatment assignment are two proposed solutions…
Linking Datasets on Organizations Using Half A Billion Open-Collaborated Records
Brian Libgober, Connor T. Jerzak
Scholars studying organizations often work with multiple datasets lacking shared identifiers or covariates. In such situations, researchers usually use approximate string ("fuzzy")…
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…
Image-based Treatment Effect Heterogeneity
Connor T. Jerzak, Fredrik Johansson, Adel Daoud
Randomized controlled trials (RCTs) are considered the gold standard for estimating the average treatment effect (ATE) of interventions. One use of RCTs is to study the causes of g…
Estimating Causal Effects Under Image Confounding Bias with an Application to Poverty in Africa
Connor T. Jerzak, Fredrik Johansson, Adel Daoud
Observational studies of causal effects require adjustment for confounding factors. In the tabular setting, where these factors are well-defined, separate random variables, the eff…