Sheldon
Howard Jacobson, Ph.D.
University
of Illinois at Urbana-Champaign
Causal Inference
1. Nikolaev, A.G., Jacobson,
S.H., Cho, W.K.T., Sauppe, J.J., Sewell, E.C., 2013
“Balance Optimization Subset Selection (BOSS): An Alternative Approach for
Causal Inference with Observational Data,” Operations
Research, 61(2), 398-412.
2. Cho, W.T., Nikolaev, A.G., Sauppe, J.,
Jacobson, S.H., Sewell, E.C., 2013, “An Optimization Approach to Making Causal Inferences,”
Statistica Neerlandica,
67(2), 211-226.
3. Sauppe, J.J., Jacobson, S.H.,
Sewell, E.C., 2014, “Complexity and Approximation Results for the Balance
Optimization Subset Selection Model for Causal Inference in Observational
Studies,” INFORMS Journal on Computing,
26(3), 547–566.
4. Dutta, S. Sauppe,
J.J, Jacobson, S.H., 2016, “Targeted Marketing Using Balance Optimization
Subset Selection,” Annals of Data Science,
3(4), 423-444.
5. Sauppe, J.J., Jacobson, S.H., 2017,
“The Role of Covariate Balance in Observational Studies,” Naval Research Logistics, 64(4), 323-344.
6. Kwon H.Y., Jacobson, S.H., Sauppe, J.J., 2019, “Bias in Balance Optimization Subset
Selection: Exploration Through Examples”, Journal
of the Operational Research Society, 70(1).
7. Kwon, H-Y.,
Sauppe, J.J., Jacobson, S.H., 2019, "Treatment Effect
Decomposition and Bootstrap Hypothesis Testing in Observational Studies," Annals
of Data Science, 6(3), 491-511.
8.
Kwon, H-Y., Sauppe, J.J.,
Jacobson, S.H., 2020, "Duality in Balance Optimization Subset Selection," Annals
of Operations Research, 289(2),
277-289.
Districting Problems
1.
King, D.M., Jacobson, S.H., Sewell, E.C., Cho, W.K. Tam, 2012,
“Geo-Graphs: An Efficient Model for Enforcing Contiguity and Hole Constraints
in Planar Graph Partitioning,” Operations
Research, 60(5), 1213-1228.
2. King, D.M., Jacobson, S.H.,
Sewell, E.C., 2015, “Efficient Geo-Graph Contiguity and Hole
Algorithms for Geographic Zoning and Dynamic Plane Graph Partitioning,” Mathematical Programming, Series A,
149(1&2), 425-457.
3. King, D.M., Jacobson, S.H.,
Sewell, E.C., 2018, “The Geo-Graph in Practice: Creating
United States Congressional Districts from Census Blocks,” Computational Optimization and Applications,
69(1), 25-49.
4. Swamy, R. King, D.M.,
Jacobson, S.H., 2023, “Multi-objective Optimization for Politically Fair Districting: A Scalable Multilevel Approach,”
Operations Research, 71(2), 536-562.
5. Ludden, I.G., Swamy, R.,
King, D.M., Jacobson, S.H., 2023, “A Bisection Protocol for Political
Districting,” INFORMS Journal on Optimization.
6. Dobbs, K., Swamy, R., King, D.M., Ludden, I.G., Jacobson, S.H., 2023, “An
Optimization Case Study in Analyzing Missouri Redistricting,” INFORMS Journal on Applied Analytics.
7. Dobbs, K., King, D.M., Jacobson, S.H., 2023, “Redistricting Optimization
with Recombination: A Local Search Case Study,” Computers and Operations Research.
8. Swamy, R., King, D.M., Ludden, I.G., Dobbs. K.W.,
Jacobson, S.H., 2024, “A Practical Optimization Framework for Political
Redistricting: A Case Study In Arizona.” Socio-Economic Planning
Sciences, (92). https://doi.org/10.1016/j.seps.2024.101836.
Election
Forecasting
1. Rigdon, S., Jacobson, S.H.,
Cho, W.T., Sewell, E.C., Rigdon, C.J., 2009, “A Bayesian Prediction Model for
the United States Presidential Election,”
American Politics Research, 37(4), 700-724.
2. Rigdon, S.E., Sewell, E.C.,
Jacobson, S.H., Cho, W.K.T., Rigdon, C.J., 2010, “An Analysis of Daily
Predictions for the 2008 United States Presidential Election,” Case Studies in Business, Industry and
Government Statistics, 4(1), 1-8.
3. Rigdon, S.E., Sauppe, J.J., Jacobson, S.H., 2015, "Forecasting the
2012 and 2014 Elections using Bayesian Prediction and Optimization," Sage Open, 5(2), 1-16.