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Bumba Mukherjee (Ph.D.; Columbia University, 2004) is a Professor at Penn State University. He was previously an Assistant Professor at the University of Notre Dame, Assistant Professor at Florida State University, a visiting research scholar at Princeton University and New York University (NYU), and a visiting fellow at the Kellogg Institute, University of Notre Dame. His research interests include studying the political economy of populism, political violence, and the link between governance and civil conflict using field experiments. He also conducts research in statistical methodology that includes spatial statistics, causal machine learning, and deep causal learning.
His work has been published in several journals such as the American Political Science Review, American Journal of Political Science, Annual Review of Political Science, Comparative Political Studies, Conflict Management and Peace Science, Experimental Economics, Journal of Development Economics, Journal of Politics, International Organization, International Interactions, International Studies Quarterly, Journal of Conflict Resolution, Mathematics, Political Analysis, R Journal, and Studies in Comparative International Development among others.
He is the author of several books including Globalization, Democracy and Trade Policy in the Developing World published by the University of Chicago Press, The Politics of Corruption in Dictatorships (with Vineeta Yadav) published by Cambridge University Press, The Politics of Mass Killing in Autocratic Regimes (with Ore Koren) published by Palgrave Macmillan, Democracy, Electoral Institutions and Judicial Empowerment in Developing Countries (with Vineeta Yadav) published by the University of Michigan Press, Principles of International Political Economy (with Jeff Kucik and Mark Hallerberg) published by Oxford University Press.
His other books include The Rise of Right-Wing Populist Parties and Reversal of Economic Reforms published by Rowman & Littlefield, The IMF, Financial Crisis, and Repression of Human Rights (with Vineeta Yadav) published by Palgrave Macmillan, Varieties of Populism and the Future of Globalization with Vineeta Yadav (Cambridge University Press), Economic Nationalism and Democracy in Uncertain Times (Edward Elgar Press)
PUBLICATIONS
Books
Bumba Mukherjee and Vineeta Yadav. Varieties of Populism and the Future of Globalization. Under Contract. New York: Cambridge University Press.
Bumba Mukherjee and Vineeta Yadav. 2024. The IMF, Financial Crisis and Repression of Human Rights. New York:
Palgrave MacMillan
Bumba Mukherjee. Economic Nationalism and Democracy in Uncertain Times. Under Contract. Cheltenham: Edward Elgar Publishing.
Vineeta Yadav and Bumba Mukherjee. 2024. The Rise of Right-Wing Populist Parties and Reversal of Economic Reforms in Developing Democracies. Lanham, MD: Rowman & Littlefield.
Bumba Mukherjee, Eric Reinhardt and Mark Hallerberg. 2020. Principles of International Political Economy. New York: Oxford University Press.
Bumba Mukherjee and Ore Koren. 2018. The Politics of Mass Killing in Autocratic Regimes. New York: Palgrave MacMillan
Bumba Mukherjee. 2016. Democracy and Trade Policy in Developing Countries. Chicago: University of Chicago Press.
Vineeta Yadav and Bumba Mukherjee. 2016. The Politics of Corruption in Dictatorships. New York: Cambridge University Press.
Vineeta Yadav and Bumba Mukherjee. 2014. Democracy, Electoral Institutions and Judicial Empowerment in Developing Countries. Michigan: University of Michigan Press.
R and Python Packages
DeepLearningCausal: Causal Inference with Super Learner and Deep Neural Networks
Functions for deep learning estimation of Conditional Average Treatment Effects (CATEs) from meta-learner models and Population Average Treatment Effects on the Treated (PATT) in settings with treatment noncompliance using reticulate, TensorFlow and Keras3. Functions in the package also implements the conformal prediction framework that enables computation and illustration of conformal prediction (CP) intervals for estimated individual treatment effects (ITEs) from meta-learner models.
BayesSPsurv: Bayesian Split Population Survival Model
Parametric spatial split-population (SP) survival models for clustered event processes. The models account for structural and spatial heterogeneity among “at risk” and “immune” populations, and incorporate time-varying covariates. Users can create their own spatial weights matrix based on their units and adjacencies of interest, making the use of these models flexible. Also includes functions for a series of diagnostic tests and plots to easily visualize spatial autocorrelation, convergence, and spatial effects.
IDCeMPy: Python Package for Inflated Discrete Choice Models
IDCeMPy provides functions to fit and assess the performance of three distinct sets of “inflated” discrete choice models: the (1) Zero-Inflated Ordered Probit (ZIOP) model without and with correlated errors (ZIOPC model) to evaluate zero-inflated ordered choice outcomes that result from a dual d.g.p, (2) Middle-Inflated Ordered Probit (MIOP) model without and with correlated errors (MIOPC) to account for the inflated middle-category in ordered choice measures related to a dual d.g.p., and (3) Generalized Inflated Multinomial Logit (GIMNL) models that account for the predominant and heterogeneous share of observations in the baseline or any lower category in unordered polytomous choice outcomes.
BayesMFSurv: Bayesian Misclassified-Failure Survival Model
Contains a split population survival estimator that models the misclassification probability of failure versus right-censored events. The split population survival estimator is described in Bagozzi et al. (2019)