Colin B. Fogarty

 


I am an Assistant Professor of Operations Research and Statistics at the MIT Sloan School of Management, and I am an affiliated faculty at the Operations Research Center at MIT. My research lies at the intersection of optimization and causal inference, leveraging advances in the former to facilitate the latter. Much of my work pertains to the design and analysis of observational studies. I am also interested in experimental design, equivalence testing, Bayesian modeling, and applications of statistics to medicine and public health.


Before joining MIT, I completed my Ph.D. in Statistics at the Wharton School of the University of Pennsylvania. I was advised by Professor Dylan S. Small.



Education


Ph.D., Statistics, The Wharton School, University of Pennsylvania, 2016

A.B., Statistics, Harvard University, 2011



Publications and Submitted Papers


Fogarty, C. Regression Assisted Inference for the Average Treatment Effect in Paired Experiments. Submitted.


Fogarty, C. Sensitivity Analysis for the Average Treatment Effect in Paired Observational Studies. Submitted.


Fogarty, C., Shi, P., Mikkelsen, M., and Small, D. Randomization Inference and Sensitivity Analysis for Composite Null Hypotheses with Binary Outcomes in Matched Observational Studies. To appear in the Journal of the American Statistical Association: Theory and Methods.


Fogarty, C. and Small, D. Sensitivity Analysis for Multiple Comparisons in Matched Observational Studies through Quadratically Constrained Linear Programming. To appear in the Journal of the American Statistical Association: Theory and Methods. DOI: 10.1080/01621459.2015.1120675


Fogarty, C., Mikkelsen, M., Gaieski, D., and Small, D. (2016). Discrete Optimization for Interpretable Study Populations and Randomization Inference in an Observational Study of Severe Sepsis Mortality. Journal of the American Statistical Association: Applications and Case Studies,111 (514), 447-458


Fogarty, C., Small, D., and Gastwirth, J. (2016). Discussion of `Perils and Potentials of Self-Selected Entry to Epidemiological Studies and Surveys' by Niels Keiding and Thomas A. Louis. Journal of the Royal Statistical Society, Series A, 179 (2), 357-358


Fogarty, C., Fay, M., Flegg, J., Stepniewska, K., Fairhurst, R. and Small, D. (2015). Bayesian Hierarchical Regression on Clearance Rates in the Presence of “Lag” and “Tail” Phases with an Application to Malaria Parasites. Biometrics, 71, 751-759.


Fogarty, C. and Small, D. (2014). Equivalence Testing for Functional Data with an Application to Comparing Pulmonary Function Devices. Annals of Applied Statistics, 8, 2002-2026.


Papers in Preparation


Olson, M., Small, D., and Fogarty, C. Multivariate Sensitivity Analysis as a Two-Player Game.


Fogarty, C. and Small, D. On the Asymptotic Distributions of Sum Statistics Under Interval and Mahalanobis Based Rerandomization. 



Awards and Honors


Statistics in Epidemiology Young Investigator Award (2016)

Awarded by the American Statistical Association section on Statistics in Epidemiology for the paper ``Sensitivity Analysis for Multiple Comparisons in Matched Observational Studies through Quadratically Constrained Linear Programming."


J. Parker Bursk Memorial Prize (2015)

Awarded by the Statistics Department at the Wharton School for excellence in research.


Donald S. Murray Prize (2015)

Awarded by the Statistics Department at the Wharton School for excellence in teaching.


Winkelman Fellowship (2013-2016)

Awarded to rising 3rd-year doctoral students who have shown substantial academic job potential across all departments at Wharton.


Harvard-Cambridge Summer Fellowship (Summer 2010)

Awarded by Harvard University to support sponsored research opportunities at the University of Cambridge.


David Rockefeller International Travel Grant Recipient (Summer 2009)

Awarded by Harvard University to support research assistantships abroad.



Teaching


Instructor


Summer 2014: Stat 101, Introductory Business Statistics (Evaluations)


Teaching Assistant


Fall 2014: Stat 550, Mathematical Statistics

Spring 2014: Stat 101, Introductory Business Statistics

Spring 2013: Stat 111, Introductory Statistics (Evaluations)

Fall 2012: Stat 431,  Statistical Inference

Spring 2012: Stat 101, Introductory Business Statistics

Fall 2011: Stat 102, Introductory Business Statistics

Spring 2011: Stat 104, Introduction to Quantitative Methods for Economics



Software


Note: many of the functions below require the R package for Gurobi, a solver which is freely available for academic use. Please update to version 6.5.0 of Gurobi before using these functions


maxbox.R defines an interpretable study population in an observational study based on a few important covariates as the solution to the ``maximal box" problem.


compositeBinary.R conducts randomization inference and a sensitivity analysis in a matched observational study with binary outcomes for the risk difference (average treatment effect), risk ratio, and the effect ratio.


sensitivitySimple.R performs a sensitivity analysis with continuous outcomes by solving a quadratic integer program.


multiCompareFunctions.R provides functions for performing a sensitivity analysis for the overall truth of multiple hypotheses through solving a quadratically constrained linear program. This script demonstrates how to use the functions by reproducing the sensitivity analysis conducted in our paper on the effect of smoking on naphthalene levels. Here is the data on naphthalene exposure in smokers and nonsmokers.













Contact


Colin B. Fogarty

E62-582

100 Main St.

Cambridge, MA 02142


cfogarty (at) mit (dot) edu


Curriculum Vitae