Optimal variational perturbations for the inference of stochastic reaction dynamics. - Christoph Zechner; Preetam Nandy; Michael Unger; Heinz Koeppl (CDC, 2012)

Strengths and limitations of microarray-based phenotype prediction: lessons learned from the IMPROVER Diagnostic Signature Challenge. - Adi L. Tarca; Mario Lauria; Michael Unger; Erhan Bilal; Stéphanie Boué; Kushal Kumar Dey; Julia Hoeng; Heinz Koeppl; Florian Martin 002; Pablo Meyer 001; Preetam Nandy; Raquel Norel; et al. (Bioinform., 2013)

A Review of Some Recent Advances in Causal Inference. - Marloes H. Maathuis; Preetam Nandy (Handbook of Big Data, 2016)

Structure Learning of Linear Gaussian Structural Equation Models with Weak Edges. - Marco Eigenmann; Preetam Nandy; Marloes H. Maathuis (UAI, 2017)

A/B Testing in Dense Large-Scale Networks: Design and Inference. - Preetam Nandy; Kinjal Basu 001; Shaunak Chatterjee; Ye Tu (NeurIPS, 2020)

Scalable Assessment and Mitigation Strategies for Fairness in Rankings. - Preetam Nandy; Amir Sepehri; Kinjal Basu 001; Heloise Logan; Deepak Agarwal; Noureddine El Karoui (CoRR, 2020)

A/B Testing for Recommender Systems in a Two-sided Marketplace. - Preetam Nandy; Divya Venugopalan; Chun Lo; Shaunak Chatterjee (NeurIPS, 2021)

Personalized Treatment Selection using Causal Heterogeneity. - Ye Tu; Kinjal Basu 001; Cyrus DiCiccio; Romil Bansal; Preetam Nandy; Padmini Jaikumar; Shaunak Chatterjee (WWW, 2021)

Long-term Dynamics of Fairness Intervention in Connection Recommender Systems. - Nil-Jana Akpinar; Cyrus DiCiccio; Preetam Nandy; Kinjal Basu 001 (AIES, 2022)

Offline Reinforcement Learning for Mobile Notifications. - Yiping Yuan; Ajith Muralidharan; Preetam Nandy; Miao Cheng; Prakruthi Prabhakar (CIKM, 2022)

Achieving Fairness via Post-Processing in Web-Scale Recommender Systems✱. - Preetam Nandy; Cyrus DiCiccio; Divya Venugopalan; Heloise Logan; Kinjal Basu 001; Noureddine El Karoui (FAccT, 2022)

Pushing the limits of fairness impossibility: Who's the fairest of them all? - Brian Hsu; Rahul Mazumder; Preetam Nandy; Kinjal Basu 001 (NeurIPS, 2022)

Generalized Causal Tree for Uplift Modeling. - Preetam Nandy; Xiufan Yu; Wanjun Liu; Ye Tu; Kinjal Basu 001; Shaunak Chatterjee (IEEE Big Data, 2023)

Detection and Mitigation of Algorithmic Bias via Predictive Parity. - Cyrus DiCiccio; Brian Hsu; YinYin Yu; Preetam Nandy; Kinjal Basu 001 (FAccT, 2023)