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CONNOR T. JERZAK

Academic Website


Search for:
MENU

 * Bio & CV

 * Research

 * Team

 * Data

 * Code

 * Courses

 * UT Austin

 * Bio & CV

 * Research

 * Team

 * Data

 * Code

 * Courses

 * UT Austin


RESEARCH AREAS

Methodological:
–Planetary causal inference
–Research design
–Text-based AI systems

Substantive:
–Descriptive representation
–Political economy
–Social movements/Globalization

[.bib]

/Methodological/SubstantivePlanetary causal inferenceDescriptive
representationResearch designPolitical economyText-based AI systemsSocial
movements & globalizationMore: [Research] [.bib]

[Bio] [CV]
[Team] [Students]
[Book Project]





ACADEMIC BACKGROUND

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, Linköping, Sweden (2021-2022)

Education:
[1] Ph.D., Government, Harvard (2021)
[2] A.M., Statistics, Harvard (2020)

[Bio] [CV]
[Team] [Students]
[Book Project]



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, Linköping, Sweden (2021-2022)

Education:

[1] Ph.D., Government, Harvard (2021)
[2] A.M., Statistics, Harvard (2020)

–





NEWS & EVENTS


2025

 * Spring – Teaching Gov 385L (Making Big Data) and Gov 391K (Machine Learning)


2024

 * December 15 – Team presenting work on multi-scale dynamics in effect
   estimation at NeurIPS workshop, Causal Representation Learning (with student
   co-author Fucheng Warren Zhu)
 * December 15 – Team presenting new work at NeurIPS workshop, Tackling Climate
   Change with Machine Learning (with graduate student co-author SayedMorteza
   Malaekeh)
 * December 14 – Invited talk at NeurIPS workshop, GenAI for Health: Potential,
   Trust and Policy Compliance
 * December 9 – Graduate student co-author SayedMorteza Malaekeh presenting
   joint work at AGU
 * December 9 – Guest lecture in PLA6009 – Environmental Data Analysis (Columbia
   University; course led by Peter Marcotullio and PhD student Kaz Sakamoto)
 * October 25 – Presenting new work on descriptive representation at IE
   University (Madrid, Spain)
 * October 19-24 – Presenting work on Global Causal Inference at the Center for
   Advanced Studies at Ludwig-Maximilians-Universität (LMU) (Munich, Germany)
 * October 17 – Presenting new work (with graduate student Beniamino Green) at
   the 2024 Record Linkage Symposium, hosted by the Initiative for Data-Driven
   Social Science (DDSS) at Princeton University
 * September 17 – Presenting new work at the GBD Science Seminar at the
   University of Washington [Tutorial Link]
 * August 23 – Presenting new work at the Indian Institute of Management
   Bangalore
 * July 18 – Presenting new work on measurement error under identification
   restrictions at Polmeth XLI [Slides]
 * July 16 – New preprint on effect heterogeneity with satellite image sequences
   in RCTs released (joint work with PhD student Ritwik Vashistha)
 * June 26 – New preprint on descriptive representation released
 * June 25 – Paper on record linkage now forthcoming at PSRM
 * June 21 – Team presenting new work on LLMs at the Sixth Workshop on NLP and
   Computational Social Science at NAACL
 * June 5 – Master’s students Cindy Conlin and Mikael P. Gustafsson successfully
   defend their theses! [PDF 1] [PDF 2]
 * June 3 – New team paper, “A Scoping Review of Earth Observation and Machine
   Learning for Causal Inference: Implications for the Geography of Poverty”,
   goes live (to appear in: Hall, Ola and Ibrahim Wahab (eds.), Geography of
   Poverty) [Data]
 * April 17 – Presenting work at the Harvard Applied Statistics Workshop
 * April 16 – Presenting work on Global Causal Inference in STAT 288 – Deep
   Statistics: AI and Earth Observations for Sustainable Development, Statistics
   Department, Harvard University [Slides]
 * April 4 – Presenting new work at the Midwest Political Science Association
   (MPSA) Conference [Slides 1] [Paper 1] [Slides 2]
 * March 25 – New arXiv preprint on LLM vs. human text analysis posted, led by
   PhD student Nicolas Audinet de Pieuchon
 * March 8 – Speaking at the Government, Public Policy, and Artificial
   Intelligence Conference [Slides]
 * January 9 – CausalImages package now fully rebuilt with a JAX backend (4 s →
   0.04 s per iteration on some hardware) and Vision Transformer backbones
 * Spring – Teaching Gov 94jc (“Making Big Data”, M 12:45-2:45 pm) at Harvard
   University [Syllabus]
 * Spring – Teaching Gov 2018 (“Introduction to Machine Learning”, W 9:45-11:45
   am) at Harvard University with Naijia Liu [Course Website]


2023

 * December 2 – Presenting new work at the Interactive Causal Learning
   Conference
 * October 24 – The DescriptiveRepresentationCalculator released on CRAN
 * October 12 – Presenting new work at the Data Analytics Colloquium
 * August 31 – Presenting “Leveraging Satellite Images in Observational Studies
   of Global Development: Challenges and Opportunities” at the Methodological
   Advances in Causal Inference panel [Slides] [Paper] [Code]
 * August 31 – Starting the graduate seminar “Statistical Analysis in Political
   Science” [Syllabus]
 * August 17 – Presenting new work at the Rand Center for Causal Inference 2023
   Symposium
 * July 9 – Presenting new work at PolMeth XL
 * July 7 – Giving a virtual short course, “Causal Inference with Satellite
   Data”, at the Society for Causal Inference [Register] [Link for Participants]
 * June 21 – “The Composition of Descriptive Representation” now forthcoming at
   American Political Science Review [PDF] [Code]
 * May 26 – Honorable Mention, Tom Ten Have Award, Society for Causal Inference
   [Details]
 * May 24 – Presenting “Image-based Treatment Effect Heterogeneity” at the 2023
   American Causal Inference Conference (ACIC) [PDF], collaborators presenting
   joint research, “Conceptualizing Treatment Leakage in Text-based Causal
   Inference” [PDF]
 * May 10 – Collaborative project recognized as a Top 100 research initiative
   with potential for significant societal impact by the Royal Swedish Academy
   of Engineering Sciences [Details]
 * April 26 – Presenting new work at the Center for Data and Methods (CMD)
   Colloquium at the University of Konstanz, Germany
 * April 25 – Guest lecturing in STAT 288 – Deep Statistics: AI and Earth
   Observations for Sustainable Development, Statistics Department, Harvard
   University [Slides]
 * April 13 – Presenting “Image-based Treatment Effect Heterogeneity” at CLeaR
   [Article PDF] [Summary PDF] [Code]
 * March 3 – Presenting new work at UT Austin’s Statistics & Data Sciences
   Seminar
 * January 9 – Started teaching the graduate seminar, “Making Big Data”
   [Syllabus]
 * January 6 – The paper, “Image-based Treatment Effect Heterogeneity”, now
   forthcoming at CLeaR, PMLR [PDF] [Code]


2022

 * November 7 – Presenting “Image-based Treatment Effect Heterogeneity” at the
   2022 Causal Data Science Meeting
 * November 5 – Presenting “Image-based Treatment Effect Heterogeneity” at the
   Texas Methods (“TexMeth”) Meeting
 * September 1 – Started teaching the graduate seminar, “Machine Learning in
   Political Science” [Syllabus]
 * September 1 – Started teaching the graduate seminar, “Statistical Analysis in
   Political Science” [Syllabus]
 * August 18 – Presenting “Image-based Treatment Effect Heterogeneity” at the
   2022 RAND Center for Causal Inference (CCI) Symposium
 * August 1 – Started an Assistant Professorship at the University of Texas at
   Austin
 * April 29 – The book chapter, “Football Fandom in Egypt”, published in
   Routledge Handbook of Sports in the Middle East [PDF]
 * July 17 – Received a Top 10% Reviewer Award from the International Conference
   on Machine Learning (ICML)
 * April 8 – The paper, “Conceptualizing Treatment Leakage in Text-based Causal
   Inference”, accepted at NAACL [PDF]
 * February 24 – Presenting “Learning to See Causal and Effect: Causal Inference
   with Images” at the Institute for Analytical Sociology, Linköping University,
   Sweden
 * February 21 – Presenting “The Composition of Descriptive Representation” at
   an ETH Zurich seminar [PDF] [Code]
 * January 7 – The paper, “An Improved Method of Automated Nonparametric Content
   Analysis for Social Science”, published in Political Analysis [PDF] [Code]


2021

 * August 18 – Started as a visiting scholar in the Program on Governance and
   Local Development (GLD) at the University of Gothenburg, Sweden
 * August 18 – Started a postdoctoral position at the AI and Global Development
   Lab, Linköping University, Sweden
 * May 29 – Graduated from Harvard University with a Ph.D. from the Department
   of Government [Dissertation]


2020

 * September 16 – Presenting “Detecting and Characterizing Latent Influence
   Dynamics in Social Science Data Using Machine Learning” at the Harvard
   Applied Statistics Workshop
 * September 13 – Presenting “Detecting and Characterizing Latent Influence
   Dynamics in Social Science Data Using Machine Learning” at APSA
 * July 14 – Presenting “Detecting and Characterizing Latent Influence Dynamics
   in Social Science Data Using Machine Learning” at PolMeth XXXVII
 * May 28 – Received an A.M. in Statistics from Harvard University
 * May 4 – The paper, “The impact of a transportation intervention on electoral
   politics: Evidence from E-ZPass”, published in Research in Transportation
   Economics [PDF] [Boston Globe Write-up]

–





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