Skip to content
View cjerzak's full-sized avatar

Organizations

@IQSS @AIandGlobalDevelopmentLab

Block or report cjerzak

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Please don't include any personal information such as legal names or email addresses. Maximum 100 characters, markdown supported. This note will be visible to only you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse
cjerzak/README.md

Bio | Papers {Substantive, Methodological} | Visualizations | Students

Bio

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 research: AI and global development, earth observation data for causal inference, adversarial dynamics, computational text analysis.

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

[CV] [Homepage] [.bib]

[Team] [Students]

[PlanetaryCausalInference.org]

[AI & Global Development Lab GitHub]

[Google Scholar] [UT Profile]

[YouTube Tutorials] [Data Assets]

Past and Present Student Co-authors or Advisees on GitHub

Cindy Conlin Andrés Cruz
Cem Mert Dallı Beniamino Green
SayedMorteza Malaekeh Nicolas Audinet de Pieuchon
Kazuki Sakamoto Ritwik Vashistha
Fucheng Warren Zhu

Papers & Code

Methodological

[Encoding Multi-level Dynamics in Effect Heterogeneity Estimation] [Video] [.bib]*

[Effect Heterogeneity with Earth Observation in Randomized Controlled Trials: Exploring the Role of Data, Model, and Evaluation Metric Choice] [.bib]*

[A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of Poverty] [.bib] [Data]* GitHub Repo stars

[Image De-confounding] [.bib] [Code] GitHub Repo stars

[Can Large Language Models (or Humans) Disentangle Text Features?] [.bib] [Code]* GitHub Repo stars

[Image-based Treatment Effect Heterogeneity] [.bib] [Code] GitHub Repo stars

[Non-parametric Content Analysis] [.bib] [Code] GitHub Repo stars

[Linking Datasets on Organizations Using Half A Billion Open Collaborated Records] [.bib] [Code] GitHub Repo stars

[Degrees of Randomness in Rerandomization Procedures] [.bib] [Code] GitHub Repo stars

Substantive

[Where Minorities are the Majority: Electoral Rules and Ethnic Representation] [.bib]

[The Composition of Descriptive Representation] [.bib] [Code] GitHub Repo stars

[Housing Values and Partisanship: Evidence from E-ZPass] [.bib]

*indicates joint work with graduate student co-author(s). See [Students] for more information.

Visualizations

Video Title

Pinned Loading

  1. iqss-research/readme-software iqss-research/readme-software Public

    Readme2: An R Package for Improved Automated Nonparametric Content Analysis for Social Science

    R 43 10

  2. causalimages-software causalimages-software Public

    causalimages: An R package for performing causal inference with image and image sequence data

    R 16 3

  3. LinkOrgs-software LinkOrgs-software Public

    LinkOrgs: An R package for linking linking records on organizations using half a billion open-collaborated records from LinkedIn

    R 11 1

  4. AIandGlobalDevelopmentLab/eo-poverty-review AIandGlobalDevelopmentLab/eo-poverty-review Public

    Directory of papers on Earth Observation (EO), Machine Learning (ML), and Causal Inference (CI)

    TeX 7

  5. DescriptiveRepresentationCalculator-software DescriptiveRepresentationCalculator-software Public

    DescriptiveRepresentationCalculator: An R package for quantifying observed and expected descriptive representation

    R 6

  6. fastrerandomize-software fastrerandomize-software Public

    FastRerandomize: An R Package for Ultra-fast Rerandomization Using Accelerated Computing

    R 6