Sheldon Howard Jacobson, Ph.D.

University of Illinois at Urbana-Champaign

 

Papers in Archival Journals

 

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., 2018, “Bias in Balance Optimization Subset Selection: Exploration Through Examples”, Journal of the Operational Research Society. 

7.  Kwon, H-Y., Sauppe, J.J., Jacobson, S.H., 2018, "Treatment Effect Decomposition and Bootstrap Hypothesis Testing in Observational Studies," Annals of Data Science.

 

 

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.

 

 

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.

 

Last Updated: 15 October 2018