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Causal Inference and Experimental Design

  1. Mukerjee, R. and Dasgupta, T. (2022), “Causal Inference from Possibly Unbalanced Split-Plot Designs: A Randomization-based Perspective" Statistica Sinica 32, 591-612. arxiv version available here.

  2. Luo, X., Dasgupta, T., Xie, M. and Liu, R. (2021), “Using confidence distribution to leverage the potential of Fisher randomization tests: inference, computation and fusion learning". Journal of the Royal Statistical Society, Series B, 83, 777-797. arxiv version available here.

  3. Branson, Z. and Dasgupta, T. (2020), “Sampling-based randomized designs for causal inference under the potential outcomes framework,” International Statistical Review, 88(1), 101-121.

  4. Lu, J., Ding, P. and Dasgupta, T. (2018) “Treatment Effects on Ordinal Outcomes: Causal Estimands and Sharp Bounds,” Journal of Educational and Behavioral Statistics, 43, 540-567.

  5. Ding, P. and Dasgupta, T (2018). “A randomization-based perspective of analysis of variance: a test statistic robust to treatment effect heterogeneity,” Biometrika, 105, 45-56.

  6. Zhao, A., Ding, P., Mukerjee, R. and Dasgupta, T. (2018), “Randomization-based Causal Inference from Split-Plot Designs." The Annals of Statistics, 46, 1876-1903.

  7. Mukerjee, R., Dasgupta, T. and Rubin, D. B. (2018), “Using Standard Tools from Finite Population Sampling to Improve Causal Inference for Complex Experiments,” Journal of the American Statistical Association (Theory and Methods), 113, 868-881.

  8. Branson, Z., Dasgupta, T. and Rubin, D.B.  (2017) “Improving Covariate Balance in 2^K factorial designs via Re-randomization,” The Annals of Applied Statistics, 10, 1958-1976.

  9. Hennessey, J., Dasgupta, T., Miratrix, L., Pattanayak, C. W., and Sarkar, P. (2016), “A conditional randomization test to account for covariate imbalance in randomized experiments,” Journal of Causal Inference, 4, 61-80.

  10. Ding, P. and Dasgupta, T. (2016), “A Potential Tale of Two by Two Tables from Completely Randomized Experiments”, Journal of the American Statistical Association (Theory and Methods), 111, 157-168.

  11. Espinosa, V., Dasgupta, T. and Rubin, D. B. (2015), “A Bayesian perspective on the analysis of unreplicated factorial designs using potential outcomes,” Technometrics,  58, 62-73. Selected for presentation in the Technometrics session at the 2015 Fall Technical Conference of the American Society for Quality at Houston, Texas.

  12. Lu, J., Ding, P. and Dasgupta, T. (2015), “Construction of alternative hypotheses for evaluation of randomization tests with ordinal outcomes,” Statistics and Probability Letters, 107, 348-355.

  13. Dasgupta, T., Pillai, N. and Rubin, D.R. (2015), “Causal Inference for 2^K factorial designs by using potential outcomes,’’ Journal of the Royal Statistical Society, Series B, 77(4), 727-753.

  14. Sabbaghi, A., Dasgupta, T. and Wu, C. F. J. (2014), “Indicator functions and the algebra of linear-quadratic parametrization”, Biometrika, 101(2), 351-363.

Miscellaneous applications

  1. Ramos RS, Cooper R, Dasgupta T, Pashley NE, Wang C. (2022), “Comparative Efficacy of Superheated Dry Steam Application and Insecticide Spray against Common Bed Bugs Under Simulated Field Conditions.” Accepted to the Journal of Economic Entomology. doi: 10.1093/jee/toac070. Epub ahead of print. PMID: 35607829.

  2. Roy, K., Ghosh, D., DeBruyn, J. M., Dasgupta, T., Wommack, K. E., Liang, X., Wagner, R. E. and Radosevich, M. (2020), “Temporal dynamics of soil virus and bacterial populations in agricultural and early plant successional soils,” Frontiers in Microbiology, 11, 1494.

  3. Mandal, P., Dasgupta, T. and Murthy, S.V.S.N. (2015), “Estimation of manpower requirement for field research: a sample survey approach,” International Journal of Industrial and Systems Engineering, 20(3), 281-305.

  4. Dasgupta, T. (2009), “A Framework for Integrating the Control and Improvement Phases of Six Sigma Using the Mahalanobis-Taguchi System,” International Journal of Industrial and Systems Engineering, 4, 615-629.

  5. Nunn, C., Thrall, P. H., Bartz, K., Dasgupta, T and Boesch, C.(2009), “Do Transmission Mechanisms or Social Systems Drive Cultural Dynamics in Socially Structured Populations?” Animal Behavior, 77, 1515-1524.

  6. Dasgupta, T. (2003), “Using the Six-Sigma metrics to measure and improve the performance of a supply chain,” Total Quality Management, 14, 355-366.

Statistical methods and applications in the physical sciences and engineering

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  1. Sengul, M. Y., Song, Y., He, L., van Duin, A., Hung, Y. and Dasgupta, T. (2021), “CLAIMED: A CLAssification-Incorporated Minimum Energy Design to explore a multivariate response surface with feasibility constraints”. IEEE Transactions on Automation Science and Engineering. doi: 10.1109/TASE.2021.3094500. arxiv version available here

  2. Sengul, Mert; Nayir, Nadire; Gao, Yawei; Hung, Ying; Dasgupta, Tirthankar; C.T. van Duin, Adri (2021), “An Initial Design-enhanced Deep Learning-based Optimization Framework to Parameterize Multicomponent ReaxFF Force Fields”. Nature Computational Materials, 7:68

  3. Sosina, S., Remillard, M., Zhang, Q., Vecitis, C. and Dasgupta, T. (2019), “Response surface optimization in the presence of internal noise with application to optimal alignment of carbon nanotubes,” Technometrics, 61, 50-65.

  4. Colosimo, B. M., Huang, Q., Dasgupta, T. and Tsung, F. (2018), “Opportunities and challenges of quality engineering in additive manufacturing,” Journal of Quality Technology, 50, 233-252

  5. Lee, I-Chen, Hong, Y., Tseng, S. and Dasgupta, T. (2018), "Sequential Bayesian Design for Accelerated Life Tests,” Technometrics, 60, 472-483

  6. Sabbaghi, A., Dasgupta, T. and Huang, Q. (2018) “Bayesian Model Building from small samples of disparate data in 3D Printing,” Tecnometrics, 60, 532-544.

  7. Remillard M., Branson, Z., Rahill, J., Zhang, Q., Dasgupta, T. and Vecitis, C. (2017), “Tuning Electric-Field Aligned CNT Architectures via Chemistry, Morphology, and Sonication from Micro to Macroscopic Scale.,” Nanoscale, 9, 6854-6865.

  8. Sosina, S., Dasgupta, T. and Huang, Q., (2016) “A stochastic graphene growth kinetics model,” Journal of the Royal Statistical Society (Series C), 65, 705-729.

  9. Remillard, M., Zhang, Q., Sosina, S., Branson, Z, Dasgupta, T., and Vecitis, C.  (2016), “Electric-field alignment of multi-walled carbon nanotubes on microporous substrates,” Carbon, 100, 578-589.

  10. Joseph, V. R., Dasgupta, T., Tuo, R. and Wu, C. F. J. (2015), “Sequential Exploration of Complex Surfaces Using Minimum Energy Designs,’’ Technometrics, 57(1), 64-74.

  11. Zhang, J., Huang, Q., Sabbaghi, A., and Dasgupta, T (2015), “Modeling, Experimental Design, and Analysis of 3D Printing Processes for Shrinkage Compensation,” IIE Transactions (Quality & Reliability Engineering), 47, 431-441. Featured article in IE magazine. Selected for presentation in the IIE Transactions session at the 2014 annual INFORMS conference at San Francisco.

  12. Huang, Q., Nouri, H., Chen, Y., Xu, K., Sosina, S. and Dasgupta, T. (2014), “Statistical Predictive Modeling and Compensation of Geometric Deviations of 3D Printed Products”, ASME Transactions, Journal of Manufacturing Science and Engineering, Special Issue on Additive Manufacturing (AM) and 3D Printing, 136(6), 1008-1:1008-9.

  13. Sabbaghi, A., Dasgupta, T., Huang, Q. and Zhang, J. (2014), “Inference for Deformation and Interference in 3D Printing”, The Annals of Applied Statistics, 8(3), 1395-1415.

  14. Zhu, L., Dasgupta, T. and Huang, Q. (2014), “A D-optimal design for estimation of parameters of an exponential-linear growth of nanostructures”, Technometrics, 56(4), 432-442. Selected for presentation in the Technometrics session at the 2014 Fall Technical Conference of the American Society for Quality at Richmond, Virginia.

  15. Dasgupta, T. and Meng, X. L. (2012), “Comment: DoIt and Do it well” (comment on Bayesian Computation Using Design of Experiments-Based Interpolation Technique by V. Roshan Joseph), Technometrics, 54, 227-231.

  16. Dasgupta, T., Adiga, N. and Wu, C. F. J. (2011), “Another closer look at Dorian Shainin’s variable search technique,” Journal of Quality Technology, 43, 273-287.

  17. Dasgupta, T., Weintraub, B. and Joseph, R. (2011), “A Physical-Statistical Model for Density Control of Nanowires,” IIE Transactions (Quality & Reliability Engineering), 43, 233-241: Featured article in IE Magazine, March 2011.

  18. Huang, Q., Wang, L., Dasgupta, T., Zhu L., Sekhar, P. K., Bhansali, S., An, Y. (2011), “Statistical Weight Kinetics Modeling and Estimation for Silica Nanowire Growth Catalyzed by Pd Thin Film”, IEEE Transactions in Automation Science and Engineering, 8, 303-310.

  19. Dasgupta, T., Miller, A. and Wu, C. F. J. (2010), “Robust Design of Measurement Systems,” Technometrics, 52, 80-93.

  20. Xu, S., Adiga, N, Ba, S., Dasgupta, T., Wu, C. F. J. and Wang, Z. L.(2009), “Optimizing and Improving the Growth Quality of ZnO Nanowire Arrays Guided by Statistical Design of Experiments” ACS Nano, 3, 1803-1812

  21. Dasgupta, T., Ma, C., Joseph, V.R., Wang, Z.L., Wu, C.F.J. (2008), “Statistical Modeling and Analysis for Robust Synthesis of Nanostructures,” Journal of the American Statistical Association (Applications and Case Studies), 103, 594-603. 

  22. Dasgupta, T. and Mandal, A. (2008), “Estimation of Process Parameters to Determine the Optimum Diagnosis Interval for Control of Defective Items,” Technometrics, 50, 167-181. 

  23. Dasgupta, T. and Wu, C.F.J. (2006), “Robust Parameter Design with Feedback Control,” Technometrics, 48, 349-360.

  24. Dasgupta, T. (2003), “An Economic Inspection Interval for Control of Defective Items in a Hot Rolling Mill,” Journal of Applied Statistics, 30, 273-282.

  25. Dasgupta, T., Sarkar, N.R., and Tamankar, K.G.T. (2002), “Using Taguchi Methods to Improve a Control Scheme by Adjustment of Changeable Settings,” Total Quality management, 13, 863-876.

  26. Dasgupta, T. and Murthy, S.V.S.N. (2001), “Looking beyond Audit-Oriented Evaluation of Gauge Repeatability and Reproducibility – A Case Study,” Total Quality Management, 12, 649-655.

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