Publications
In reverse chronological order. Please email me if any of the links are broken. * Graduate student.
Github: https://github.com/bikram12345k includes code to implement the proposed methods in the published papers.
Selected Preprints (please email for the latest version)
Under review:
Y. Ohnishi*, W. Kar and B. Karmakar. “Inferring causal effect of a digital communication strategy under a latent sequential ignorability assumption and treatment noncompliance.” Under review.
Y. Ohnishi*, B. Karmakar and A. Sabbaghi. “Degree of interference: A general framework for causal inference under interference.” Under review.
B. Karmakar. “Regression to the mean in regression discontinuity design: Bias and sensitivity analysis.” Under review.
B. Karmakar and B. Pareek. “Leaf nodes of decision trees as balancing score in observational studies with multiple treatments.” Under review.
Ghosh, A.*, Deb, N., Karmakar, B., and Sen, B.. Efficiency of Regression (Un)-Adjusted Rosenbaum’s Rank-based Estimator in Randomized Experiments. [arxiv]
Revision submitted:
Z. Qin* and B. Karmakar. (2023). “Causal inference with confounded treatment by calibrating resistant population’s variance.” Journal of the Royal Statistical Society Series B, Reject and resubmit; revision submitted.
B. Karmakar, G. Mukherjee, and W. Kar. (2023). “Using penalized synthetic controls on truncated data: A case study on effect of marijuana legalization on direct payments to physicians by opioid manufacturers.” Journal of the American Statistical Association, Major revision; revision submitted. [paper]
B. Karmakar, A. G. Zauber, A. I. Hahn, Y. K. Lau, D. A. Corley, C. A. Doubeni and M. M. Joffe. (2023). “Bias due to coarsening of time intervals in the inference for the efficiency of colorectal cancer screening.” International Journal of Epidemiology, Major revision; revision submitted.
Karmakar, B. (2023) “Evidence factors.” In: J.R. Zubizarreta, E.A. Stuart, D.S. Small, P.R. Rosenbaum (Eds.), Handbook of Matching and Weighting Adjustments for Causal Inference, Chapman and Hall/CRC, pp. 583–609. [link]
Karmakar, B. and D. S. Small (2023). Constructing independent evidence from regression and instrumental variables with an application to the effect of violent conflict on altruism and risk preference. Biostatistics & Epidemiology. Available from: https://doi.org/10.1080/24709360.2022.2109910. [paper] [journal]
Karmakar, B. (2022). An approximation algorithm for blocking of an experimental design. Journal of the Royal Statistical Society – Series B. 84(5), 1726–1750. (R code link) [paper] [journal]
Zhao, A.,Y. Lee, D. S. Small and B. Karmakar (2022). Evidence factors from multiple, possibly invalid instrumental variables. The Annals of Statistics, 50(3), 1266-1296. [paper] [journal]
Youjin Lee’s Talk [video][Slides, external link]. Discussion by José Zubizarreta on the talk by Youjin Lee [link].
Karmakar B, P. Liu, G. Mukherjee, S. Dutta and H. Che (2022). Improved retention analysis in Freemium role-playing games by jointly modeling players’ motivation, progression and churn. Journal of the Royal Statistical Society – Series A, 185, 102-133.[paper (external link)] [journal]
Karmakar, B., D. S. Small and P. R. Rosenbaum (2021). Reinforced designs: Multiple instruments plus control groups as evidence factors in an observational study of the effectiveness of Catholic schools. Journal of the American Statistical Association, 116(533), 82–92. (R package blockingChallenge, includes replication code. This package can be used to create blocked designs with many covariates.) [paper] [journal]
Seminar at MRC Integrative Epidemiology Unit; video link here; Causal inference group, JHU; video link here.
Karmakar, B., C. A. Doubeni and D. S. Small (2020). Evidence factors in a case-control study with application to the effect of flexible sigmoidoscopy screening on colorectal cancer. Annals of Applied Statistics, 14, 829–849. (replication code included in the R package evidenceFactors.) [paper] [journal]
Karmakar, B., and D. S. Small (2020). Assessment of the extent of corroboration of an elaborate theory of a causal hypothesis using partial conjunctions of evidence factors. The Annals of Statistics, 48, 3283–3311. [paper] [journal]
Dylan Small’s Talk [video]. Discussion by Peter Bühlmann on the talk by Dylan Small based on this paper [link].
Karmakar, B., D. S. Small and P. R. Rosenbaum (2020). Using evidence factors to clarify exposure biomarkers. American Journal of Epidemiology, 189, 243–249. [paper] [journal]
Karmakar, B., B. French and D. S. Small (2019). Integrating the evidence from evidence factors in observational studies. Biometrika, 1066, 353–367. (R package evidenceFactors.) [paper] [journal]
Karmakar, B., D. S. Small and P. R. Rosenbaum (2019). Using approximation algorithms to build evidence factors and related designs for observational studies. Journal of Computational and Graphical Statistics, 28, 698–709. (R package approxmatch, includes replication code. This package can be used to build matched designs with multiple treatment levels, use version 2.0.) [paper] [journal]
Karmakar, B., S. Das, S. Bhattacharya, R. Sarkar and I. Mukhopadhyay (2019). Tight clustering for large data sets with an application to gene expression data. Nature–Scientific Reports, 9, 3053. (Code)
Karmakar, B., R. Heller and D. S. Small (2018). False discovery rate control for effect modification in observational studies. Electronic Journal of Statistics, 12, 3232–3253.
Karmakar, B. and N. R. Pal (2018). How to make a neural network say “Don’t know”? Information Sciences, Vol. 430-431, 444–466.
Karmakar, B. and I. Mukhopadhyay (2018). Risk efficient estimation of fully dependent random coefficient autoregressive models of general order. Communications in Statistics – Theory and Methods, 47, 4242–4253.
Karmakar, B. and I. Mukhopadhyay (2016). An efficient partition – repetition approach in clustering of big data, in: S. Pyne, B.L.S. Prakasa Rao, S.B. Rao (Eds.), Big data analytics: Methods and applications, Springer India, New Delhi, 2016, pp. 75–93. doi:10.1007/978-81-322-3628-3 5
Karmakar, B., K. Dhara, K. K. Dey, A. Basu and A. K. Ghosh (2015). Tests for statistical significance of a treatment effect in the presence of hidden sub-populations. Statistical Methods & Applications, 24, 97–119.