Jason Poulos

Postdoctoral Fellow,
Harvard Medical School

I'm a Postdoctoral Fellow in Data Science in the Department of Health Care Policy at Harvard Medical School.

After receiving my PhD from UC Berkeley in 2019, I held a joint postdoctoral appointment in the Department of Statistical Science at Duke University and the Statistical and Applied Mathematical Sciences Institute (SAMSI), where I participated in the Causal Inference and Deep Learning programs.

My research focuses on leveraging machine (deep) learning for improving causal inference in observational studies in the social sciences and health data sciences.

I'm on the 2022-23 academic job market and also interested in research scientist roles in industry.


Are Deep Learning Models Superior for Missing Data Imputation in Surveys? Evidence from an Empirical Comparison.
(with Zhenhua Wang, Olanrewaju Akande, and Fan Li). Survey Methodology, 2022.
[Code and Supplementary Material]

RNN-Based Counterfactual Prediction, with an Application to Homestead Policy and Public Schooling.
(with Shuxi Zeng). Journal of the Royal Statistical Society, 2021.
[arXiv] [Code]

Amnesty Policy and Elite Persistence in the Postbellum South: Evidence from a Regression Discontinuity Design. Journal of Historical Political Economy, 2021.
[arXiv] [Code]

Character-Based Handwritten Text Transcription with Attention Networks.
(with Rafael Valle). Neural Computing & Applications, 2021.
[arXiv] [Code]

Estimating Population Average Treatment Effects from Experiments with Noncompliance.
(with Kellie Ottoboni). Journal of Causal Inference, 2020.
[arXiv] [Code]

Land Lotteries, Long-Term Wealth, and Political Selection. Public Choice, 2019.

Missing Data Imputation for Supervised Learning.
(with Rafael Valle). Applied Artificial Intelligence, 2018.
[arXiv] [Supplementary Material] [Code]


Targeted Learning in Observational Studies with Multi-Level Treatments: An Evaluation of Antipsychotic Drug Treatment Safety for Patients with Serious Mental Illnesses.
(with Marcela Horvitz-Lennon, Katya Zelevinsky, Thomas Huijskens, Pooja Tyagi, Jiaju Yan, Jordi Diaz, Tudor Cristea-Platon, and Sharon-Lise Normand).

Gender Gaps in Frontier Entrepreneurship? Evidence from 1901 Oklahoma Land Lottery Winners.

Adversarial Machine Learning: Perspectives from Adversarial Risk Analysis.
(with David Rios Insua, Roi Naveiro, and Victor Gallego).

Retrospective Causal Inference via Matrix Completion, with an Evaluation of the Effect of European Integration on Labour Market Outcomes.
(with Andrea Albanese, Fan Li, and Andrea Mercatanti).

State-Building through Public Land Disposal? An Application of Matrix Completion for Counterfactual Prediction.