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R and Python Packages

Nguyen Huynh, Yang Yang, Bumba Mukherjee

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.

Article: Nguyen Khoi Huynh, Yang Yang, and Bumba Mukherjee. 2026. "DeepLearningCausal: R Package for Estimating Treatment Effects Using Deep Neural Networks and Ensemble Learning." The R Journal 18(2): 103-122. 

GitHub: DeepLearningCausal: Nguyen Khoi Huynh, Yang Yang, and Bumba Mukherjee

Brandon L. Bolte, Nicolas Schmidt, Sergio Bejar, Minnie M. Joo, Nguyen Huynh, Bumba Mukherjee

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.

Article:  Brandon L. Bolte, Nicolas Schmidt, Sergio Bejar, Nguyen Huynh, and Bumba Mukherjee. 2021. "BayesSPsurv: An R Package to Estimate Bayesian Split-Population (Spatial) Survival Models for Clustered Event Processes.'' The R Journal 13(1): 595-613

GitHub: BayesSPsurv: Nicolas Schmidt, Sergio Bejar, Nguyen Huynh, Brandon Bolte

Nguyen Huynh, Sergio Bejar, Vineeta Yadav, Bumba Mukherjee

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.

Article:  Nguyen K. Huynh, Sergio Bejar, Vineeta Yadav, and Bumba Mukherjee. 2021. “IDCeMPy: Python Package for Inflated Discrete Choice Models.” Journal of Open Source Software 6(63): 3322-3327.

GitHub: idcempy:Nguyen K. Huynh, Sergio Bejar, Vineeta Yadav, Bumba Mukherjee

Minnie M. Joo, Sergio Bejar, Nicolas Schmidt, Vineeta Yadav, Bumba Mukherjee

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)

Article:  Brandon L. Bolte, Nicolas Schmidt, Sergio Bejar, Nguyen Huynh, and Bumba Mukherjee. 2021. "BayesSPsurv: An R Package to Estimate Bayesian Split-Population (Spatial) Survival Models for Clustered Event Processes.'' The R Journal 13(1): 595-613

GitHub: BayesMFSurv:  Nicolas Schmidt and Sergio Bejar

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