Design and Measurement - Connor T. Jerzak

Overview: These papers develop new tools for designing and learning from experiments and quasi-experiments when real data are messy—treatments are high-dimensional or hard to randomize perfectly, key predictors are measured with error, and researchers still need credible uncertainty estimates. Across rerandomization, latent-variable measurement, and decision settings, the goal is the same: use new theory, accelerated computing, and modern stochastic optimization to improve balance, sharpen statistical power, and make inference more informative and trustworthy at scale.

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Markus Pettersson, Connor T. Jerzak, Adel Daoud. Debiasing Machine Learning Predictions for Causal Inference Without Additional Ground Truth Data: 'One Map, Many Trials' in Satellite-Driven Poverty Analysis. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI-26), 2026.

@article{pettersson2026debiasing,
  title={Debiasing Machine Learning Predictions for Causal Inference Without Additional Ground Truth Data: 'One Map, Many Trials' in Satellite-Driven Poverty Analysis},
  author={Pettersson, Markus and Connor T. Jerzak and Adel Daoud},
  journal={Proceedings of the AAAI Conference on Artificial Intelligence (AAAI-26)},
  year={2026},
  volume={},
  pages={},
  publisher={}
}

[Overview][Code][>]

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Connor T. Jerzak, Rebecca Goldstein, Aniket Kamat, Fucheng Warren Zhu. FastRerandomize: Fast Rerandomization Using Accelerated Computing. SoftwareX, 33: 102508, 2026.

@article{jerzak2026fastrerandomize,
  title={FastRerandomize: Fast Rerandomization Using Accelerated Computing},
  author={Jerzak, Connor T. and Rebecca Goldstein and Aniket Kamat and Fucheng Warren Zhu},
  journal={SoftwareX},
  year={2026},
  volume={33},
  pages={102508},
  publisher={}
}

[Overview][Code][>]
[Website]

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Connor T. Jerzak, Stephen Jessee. Correcting Bias When Using Latent Regressors. Political Analysis, 2026.

@article{connor t. jerzak 2026correcting,
  title={Correcting Bias When Using Latent Regressors},
  author={Connor T. Jerzak and Stephen Jessee},
  journal={Political Analysis},
  year={2026},
  pages={},
  publisher={}
}

[Overview][Code]

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Connor T. Jerzak, Priyanshi Chandra, Rishi Hazra. MiniMax Learning of Interpretable Factored Stochastic Policies from Conjoint Data, with Uncertainty Quantification. International Conference on Machine Learning (ICML), 2026.

@article{connor t. jerzak 2026minimax,
  title={MiniMax Learning of Interpretable Factored Stochastic Policies from Conjoint Data, with Uncertainty Quantification},
  author={Connor T. Jerzak and Priyanshi Chandra and Rishi Hazra},
  journal={International Conference on Machine Learning (ICML)},
  year={2026},
  volume={},
  pages={},
  publisher={}
}

[Overview][.mp3][Website]

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Connor T. Jerzak, Gary King, Anton Strezhnev. An Improved Method of Automated Nonparametric Content Analysis for Social Science. Political Analysis, 31(1): 42-58, 2023.

@article{jerzak2023improved,
  title={An Improved Method of Automated Nonparametric Content Analysis for Social Science},
  author={Jerzak, Connor T. and Gary King and Anton Strezhnev},
  journal={Political Analysis},
  year={2023},
  volume={31},
  number={1},
  pages={42-58},
  publisher={}
}

[Overview][Data][Code][>]

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Satiyabooshan Murugaboopathy, Connor T. Jerzak, Adel Daoud. Platonic Representations for Poverty Mapping: Unified Vision-Language Codes or Agent-Induced Novelty?. Proceedings of the 7th International Conference on Social Computing (ICSC 2026), of Communications in Computer and Information Science, 2026.

@article{murugaboopathy2026platonic,
  title={Platonic Representations for Poverty Mapping: Unified Vision-Language Codes or Agent-Induced Novelty?},
  author={Murugaboopathy, Satiyabooshan and Connor T. Jerzak and Adel Daoud},
  journal={Proceedings of the 7th International Conference on Social Computing (ICSC 2026), of Communications in Computer and Information Science},
  year={2026},
  volume={},
  pages={},
  publisher={Springer Nature}
}

[Overview][Data][Slides]
[Video]

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Connor T. Jerzak, Rebecca Goldstein. Degrees of Randomness in Rerandomization Procedures. ArXiv Preprint, 2023.

@article{jerzak2023degrees,
  title={Degrees of Randomness in Rerandomization Procedures},
  author={Jerzak, Connor T. and Rebecca Goldstein},
  journal={ArXiv Preprint},
  year={2023},
  volume={},
  pages={},
  publisher={}
}

[Overview]

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FastRerandomize Performance: n = 100

Rerandomization performance results for n = 100 (light mode)

Rerandomization performance results for n = 100 (dark mode)

FastRerandomize Performance: n = 1000

Rerandomization performance results for n = 1000 (light mode)

Rerandomization performance results for n = 1000 (dark mode)

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