9 citations · 47 across the 12 of their papers we have counts for
8 papers · 1 filter
Detection and Mitigation of Algorithmic Bias via Predictive Rate Parity
Cyrus DiCiccio, Brian Hsu, YinYin Yu +2
Predictive parity (PP), also known as sufficiency, is a core definition of algorithmic fairness essentially stating that model outputs must have the same interpretation of expected…
Generalized Causal Tree for Uplift Modeling
Preetam Nandy, Xiufan Yu, Wanjun Liu +3
Uplift modeling is crucial in various applications ranging from marketing and policy-making to personalized recommendations. The main objective is to learn optimal treatment alloca…
A/B Testing in Dense Large-Scale Networks: Design and Inference
Preetam Nandy, Kinjal Basu, Shaunak Chatterjee +1
Design of experiments and estimation of treatment effects in large-scale networks, in the presence of strong interference, is a challenging and important problem. Most existing met…
Inference for Individual Mediation Effects and Interventional Effects in Sparse High-Dimensional Causal Graphical Models
Abhishek Chakrabortty, Preetam Nandy, Hongzhe Li
We consider the problem of identifying intermediate variables (or mediators) that regulate the effect of a treatment on a response variable. While there has been significant resear…
Robust causal structure learning with some hidden variables
Benjamin Frot, Preetam Nandy, Marloes H. Maathuis
We introduce a new method to estimate the Markov equivalence class of a directed acyclic graph (DAG) in the presence of hidden variables, in settings where the underlying DAG among…
Structure Learning of Linear Gaussian Structural Equation Models with Weak Edges
Marco F. Eigenmann, Preetam Nandy, Marloes H. Maathuis
We consider structure learning of linear Gaussian structural equation models with weak edges. Since the presence of weak edges can lead to a loss of edge orientations in the true u…
A review of some recent advances in causal inference
Marloes H. Maathuis, Preetam Nandy
We give a selective review of some recent developments in causal inference, intended for researchers who are not familiar with graphical models and causality, and with a focus on m…
Estimating the effect of joint interventions from observational data in sparse high-dimensional settings
Preetam Nandy, Marloes H. Maathuis, Thomas S. Richardson
We consider the estimation of joint causal effects from observational data. In particular, we propose new methods to estimate the effect of multiple simultaneous interventions (e.g…