Yaroslav Mukhin

Yaroslav Mukhin

Assistant Research Professor

Cornell University

I am an Assistant Research Professor at Cornell University, affiliated with the Center for Data Science for Enterprise and Society, the Department of Economics, and the Department of Statistics. I work at the intersection of statistics, econometrics, and machine learning. My research develops methods for efficient and robust inference, with a focus on influence functions, kernel methods, and causal inference. I received my PhD in Economics and Statistics from MIT. Previously, I taught statistics at Dartmouth College and MIT IDSS.

Research

Citations (BibTeX)

Download .bib file

% Citation keys use first-author surname + four-digit year + short title keyword.

@misc{mukhin2026surrogates,
  title = {{Surrogate-powered Causal Inference with Censored Outcomes}},
  author = {Yaroslav Mukhin and Tereza Oprea and Arielle Anderer and Christina Lee Yu and Jelena Bradic},
  year = {2026},
  eprint = {2610.05486},
  archivePrefix = {arXiv},
  primaryClass = {math.ST},
  url = {https://arxiv.org/abs/2610.05486}
}

@inproceedings{mukhin2025kernel,
  title = {{Kernel von Mises Formula of the Influence Function}},
  author = {Yaroslav Mukhin},
  year = {2025},
  booktitle = {Advances in Neural Information Processing Systems},
  volume = {38},
  doi = {10.52202/085713-0324},
  url = {https://papers.nips.cc/paper_files/paper/2025/hash/0dde49ec491174a11272c5e1e6013f9f-Abstract-Conference.html}
}

@inproceedings{cho2024kernel,
  title = {{Kernel Debiased Plug-in Estimation: Simultaneous, Automated Debiasing without Influence Functions for Many Target Parameters}},
  author = {Brian M. Cho and Yaroslav Mukhin and Kyra Gan and Ivana Malenica},
  year = {2024},
  booktitle = {Proceedings of the 41st International Conference on Machine Learning},
  series = {Proceedings of Machine Learning Research},
  volume = {235},
  pages = {8534--8555},
  publisher = {PMLR},
  url = {https://proceedings.mlr.press/v235/cho24c.html}
}

@misc{immorlica2026learning,
  title = {{Learning from a Mixture of Information Sources}},
  author = {Nicole Immorlica and Brendan Lucier and Yaroslav Mukhin and Clayton Thomas and Ruqing Xu},
  year = {2026},
  eprint = {2609.33214},
  archivePrefix = {arXiv},
  primaryClass = {econ.TH},
  note = {Extended abstract forthcoming at WINE 2026},
  url = {https://arxiv.org/abs/2609.33214}
}

@misc{mukhin2021robustness,
  title = {{On Robustness of Counterfactuals in Structural Models}},
  author = {Yaroslav Mukhin},
  year = {2021},
  note = {NeurIPS workshop contribution; not a main-conference proceedings paper},
  url = {https://sites.google.com/view/robustbayes-neurips21/accepted-papers}
}

@phdthesis{mukhin2019geometric,
  title = {{Geometric Methods in Econometrics and Statistics}},
  author = {Yaroslav Mukhin},
  year = {2019},
  school = {Massachusetts Institute of Technology},
  type = {PhD thesis in Economics and Statistics},
  url = {https://hdl.handle.net/1721.1/124058}
}

@misc{mukhin2018sensitivity,
  title = {{Sensitivity of Regular Estimators}},
  author = {Yaroslav Mukhin},
  year = {2018},
  eprint = {1805.08883},
  archivePrefix = {arXiv},
  primaryClass = {econ.EM},
  url = {https://arxiv.org/abs/1805.08883}
}

Surrogate-powered Causal Inference with Censored Outcomes

PDF

With Tereza Oprea, Arielle Anderer, Christina Lee Yu, Jelena Bradic.

Preprint, .

Using post-treatment histories to recover information lost to censoring, without changing the marginal causal estimand.

About the header graphic

The header adapts the observed-data panel of my “Stopped trajectories” slide, with the probability cloud and censoring cuts added from its upper panel.

The probability space maps to three illness–death paths. Blue: recurrence at 3, observed death at 6, censoring at 8. Orange: censoring at 4, hidden death at 7.5. Purple: recurrence at 2, censoring at 5, hidden death at 9.
Solid segments are observed. Pale dashed segments are illustrative hidden continuations, not estimates. Vertical dotted lines mark censoring times; open circles mark the two censored endpoints. The blue path remains in its known absorbing state because death precedes censoring.

Website TikZ source Original slide frame

Sensitivity of Regular Estimators

PDF

Yaroslav Mukhin.

Preprint, .

A geometric measure of local sensitivity linking changes in target and control parameters to influence functions and asymptotic covariance.

Work in progress

Selected talks

Teaching

I have taught statistics in the Department of Economics at Dartmouth College and at MIT IDSS.

Running

  • Pemigewasset Loop New Hampshire, USA
    9:04:56
  • McKirdy Micro 10K Rockland Lake State Park, Valley Cottage, New York
    35:35
  • NYRR Van Cortlandt Park 5K The Bronx, New York
    19:05
  • Valencia Marathon Valencia, Spain
    2:42:24
  • Cambridge Half Marathon Cambridge, Massachusetts
    1:17:35
  • New Bedford Half Marathon New Bedford, Massachusetts
    1:18:17