Colin Fogarty


Colin Fogarty


Colin Fogarty is an Assistant Professor of Operations Research and Statistics at the MIT Sloan School of Management.

Colin's research interests lie in the design and analysis both of randomized experiments, and of observational studies while assessing the robustness of a study's findings to hidden biases. Much of his work explores the extent to which classical randomization-based approaches for inference in experiments and observational studies extend to circumstances where heterogeneous treatment effects are suspected, as is common in practice. His work also illustrates tangible benefits for many quasi-experimental devices in terms of improved robustness to lurking variables in observational studies.

Colin received his AB in Statistics from Harvard University and his PhD in Statistics from the Wharton School of the University of Pennsylvania.


"Extended Sensitivity Analysis for Heterogeneous Unmeasured Confounding with an Application to Sibling Studies of Returns to Education."

Fogarty, Colin B., and Raiden B. Hasegawa. Annals of Applied Statistics. Forthcoming. arXiv Preprint.

"Malaria Parasite Clearance Rate Regression: An R Software Package for a Bayesian Hierarchical Regression Model."

Sharifi-Malvajerdi, Saeed, Feiyu Zhu, Colin B. Fogarty, Michael P. Fay, Rick M. Fairhurst, Jennifer A. Flegg, Kasia Stepniewska, and Dylan S. Small. Malaria Journal Vol. 18, No. 4 (2019): 1-16. Download Paper.

"On Mitigating the Analytical Limitations of Finely Stratified Experiments."

Fogarty, Colin. Journal of the Royal Statistical Society: Series B (Statistical Methodology) Vol. 80, No. 5 (2018): 1035-1056. arXiv Preprint.

"Randomization Inference and Sensitivity Analysis for Composite Null Hypotheses with Binary Outcomes in Matched Observational Studies."

Fogarty, Colin, Pixu Shi, Mark Mikkelsen, and Dylan Small. Journal of the American Statistical Association: Theory and Methods Vol. 112, No. 517 (2017): 321-331.

"Sensitivity Analysis for Multiple Comparisons in Matched Observational Studies through Quadratically Constrained Linear Programming."

Fogarty, Colin, and Dylan S. Small. Journal of the American Statistical Association Vol. 111, No. 516 (2017): 1820-1830.

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