Bio | Papers | Visuals | Students

Present:
[1.] Assistant Professor in the Department of Government at the University of Texas at Austin.
[2.] Consultant, Institute for Health Metrics & Evaluation (IHME), University of Washington.

Past:
[1.] Visiting Assistant Professor in the Department of Government at Harvard University (2024).
[2.] Postdoc, AI & Global Development Lab (2021-2022).

Methodological work: AI and global development, EO for causal inference, adversarial dynamics, computational text analysis.

Substantive work: Political economy, social movements, descriptive representation.

[CV] [Homepage] [.bib] [logs]

[Team] [Students]

[PlanetaryCausalInference.org]

[AI & Global Development Lab GitHub]

[Google Scholar] [UT Profile]

[YouTube Tutorials] [Data Assets]

• Nicolas Audinet de Pieuchon presents: Benchmarking Debiasing Methods for LLM-based Parameter Estimates
• Nicolas Audinet de Pieuchon presents: Can Large Language Models (or Humans) Disentangle Text?
• Adel Daoud presents: A First Course in Planetary Causal Inference: Confounding (@IC2S2 2025)
• Adel Daoud presents: Planetary Causal Inference: Overview (@Yale)
• Connor Jerzak presents: Seeing Like a Satellite While Learning Across Scales: Remote Audits + Multi-Scale Optimization for Heterogeneity (@Columbia)
• Connor Jerzak presents: Selecting Optimal Candidate Profiles in Adversarial Environments (@UT Dallas & National Chung Hsing University)
• Richard Johansson presents: Conceptualizing Treatment Leakage in Text-based Causal Inference (@NAACL)
• Satiyabooshan Murugaboopathy presents: Platonic Representations for Poverty Mapping: Unified Vision-Language Codes or Agent-Induced Novelty?
• Kazuki Sakamoto presents: A Scoping Review of Earth Observation and Machine Learning for Causal Inference
• Fucheng Warren Zhu presents: Optimizing Multi-Scale Representations to Detect Effect Heterogeneity Using EO and Computer Vision

• Benchmarking Debiasing Methods for LLM-based Parameter Estimates (EMNLP 2025) – Nicolas Audinet de Pieuchon, Adel Daoud, Connor T. Jerzak, Moa Johansson, Richard Johansson. This paper benchmarks different techniques for removing bias from LLM-generated labels, showing that combining large-scale LLM annotations with a modest number of expert labels can reduce bias and improve parameter estimation. [PDF] [.bib] [Video] [Slides] [Data]

• Can Large Language Models (or Humans) Disentangle Text? (NLP+CSS 2024) – Nicolas Audinet de Pieuchon, Adel Daoud, Connor T. Jerzak, Moa Johansson, Richard Johansson. Investigates whether LLMs or humans can separate intertwined textual features and highlights the limitations of current models. [PDF] [.bib] [Code]

• Conceptualizing Treatment Leakage in Text-Based Causal Inference (NAACL 2022) – Adel Daoud, Connor T. Jerzak, Richard Johansson. Introduces the notion of treatment leakage when causal treatments are encoded in text, offering guidance on designing text-based experiments to minimize leakage. [PDF] [.bib] [Video]

• Linking Datasets on Organizations Using Half a Billion Open-Collaborated Records (PSMR 2024) – Brian Libgober, Connor T. Jerzak. Describes methods for linking disparate organizational data sets using over 500 million open-collaborated records. [PDF] [.bib] [Code]

• An Improved Method of Automated Non-Parametric Content Analysis for Social Science (Political Analysis 2023) – Connor T. Jerzak, Gary King, Anton Strezhnev. Presents an enhanced non-parametric content-analysis approach that automates the extraction of substantive information from text. [PDF] [.bib] [Code]

• Planetary Causal Inference: Understanding the Environment, Society, and Economy through Earth Observation and AI Systems (Cambridge University Press 2026+) – Connor T. Jerzak, Adel Daoud. Book project on Planetary Causal Inference, capturing efforts in an emerging field to combine satellite imagery and planetary-scale data sources with localized studies, especially RCTs, to derive insights about causality. [More]

• Debiasing Machine Learning Predictions for Causal Inference Without Additional Ground Truth Data: “One Map, Many Trials” in Satellite-Driven Poverty Analysis (AAAI 2026) – Markus Pettersson, Connor T. Jerzak, Adel Daoud. Proposes methods to debias ML predictions for causal inference in poverty mapping. [PDF] [.bib] [Code]

• Optimizing Multi-Scale Representations to Detect Effect Heterogeneity Using Earth Observation and Computer Vision: Applications to Two Anti-Poverty RCTs (CLeaR 2025) – Warren Zhu Fucheng, Connor T. Jerzak, Adel Daoud. Develops multi-scale image representations to discover treatment effect heterogeneity in randomized controlled trials. [PDF] [Video] [.bib]

• A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of Poverty (2025) – Kazuki Sakamoto, Connor T. Jerzak, Adel Daoud. Reviews the literature on combining earth-observation data with machine learning for causal inference. [PDF] [.bib] [Data]

• 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. Examines how historical data, model selection and evaluation metrics affect detection of effect heterogeneity when using earth-observation images. [PDF] [.bib]

• Image-based Treatment Effect Heterogeneity (CLeaR 2023) – Connor T. Jerzak, Fredrik Johansson, Adel Daoud. Introduces methods for estimating heterogenous treatment effects directly from images. [PDF] [.bib] [Code]

• Integrating Earth Observation Data into Causal Inference: Challenges and Opportunities – Connor T. Jerzak, Fredrik Johansson, Adel Daoud. Outlines challenges and strategies for incorporating satellite imagery into causal inference and shows that high-resolution satellite data can help adjust for confounders. [PDF] [.bib] [Code]

• Platonic Representations for Poverty Mapping: Unified Vision-Language Codes or Agent-Induced Novelty? – Satiyabooshan Murugaboopathy, Connor T. Jerzak, Adel Daoud. Investigates whether unified vision–language models can represent poverty or if generative agents create novel representations. [PDF] [.bib] [Data]

• FastRerandomize: Fast Rerandomization Using Accelerated Computing (SoftwareX 2026) – Connor T. Jerzak, Rebecca Goldstein, Aniket Kamat, Fucheng Warren Zhu. Presents an efficient algorithm for rerandomization of experiments, leveraging accelerated computing. [PDF] [.bib] [Code]

• Selecting Optimal Candidate Profiles in Adversarial Environments Using Conjoint Analysis and Machine Learning – Connor T. Jerzak, Priyanshi Chandra, Rishi Hazra. Develops a framework to identify candidate profiles that remain robust when voters evaluate profiles strategically. [PDF] [.bib]

• Attenuation Bias with Latent Predictors – Connor T. Jerzak, Stephen Jessee. Explores how measurement error in latent predictors can attenuate causal estimates and proposes corrections. [PDF] [.bib]

• CausalImages: An R Package for Causal Inference with Earth Observation, Biomedical and Social Science Images – Connor T. Jerzak, Adel Daoud. Introduces an R package for performing causal inference directly on image and image-sequence data. [PDF] [.bib] [Code]

• Where Minorities are the Majority: Electoral Rules and Ethnic Representation (2024) – John Gerring, Alan Hicken, Connor T. Jerzak, Robert Moser, Erzen Öncel. Studies how electoral rules affect ethnic minority representation when minorities constitute a local majority. [PDF] [.bib]

• The Composition of Descriptive Representation (APSR 2024) – John Gerring, Connor T. Jerzak, Erzen Öncel. Analyzes how descriptive representation is composed and which demographic attributes drive voters’ preferences. [PDF] [.bib] [Code]

• The Impact of a Transportation Intervention on Electoral Politics: Evidence from E-ZPass (Research in Transportation Economics 2020) – Connor T. Jerzak, Brian Libgober. Assesses how the introduction of the E-ZPass toll system influenced housing values and partisan voting patterns. [PDF] [.bib]

• Football fandom in Egypt (Routledge Handbook of Sport in the Middle East 2022) – Connor T. Jerzak. Examines the intersection of football fandom and social identity in Egypt. [PDF] [.bib]

Planetary Causal Inference Workflow	Institutional Analysis
Fast Rerandomization with Accelerated Computing	Effect Heterogeneity with Image Sequences
PSRM 2024	ACL Anthology
PCI Book Launch