1 citations · 1 across the 4 of their papers we have counts for
4 papers
A Framework for Covariate-Adjusted Bivariate Causal Discovery
Soumik Purkayastha, Peter X. -K. Song
Ascertaining causal direction from observational data is a fundamental challenge in scientific inquiry. Of particular interest is the problem of covariate-adjusted bivariate causal…
Quantification and cross-fitting inference of asymmetric relations under generative exposure mapping models
Soumik Purkayastha, Peter X. -K. Song
Learning directionality between variables is crucial yet challenging, especially for mechanistic relationships without a priori ordering assumptions. We propose a coefficient of as…
fastMI: a fast and consistent copula-based estimator of mutual information
Soumik Purkayastha, Peter X. K. Song
As a fundamental concept in information theory, mutual information () has been commonly applied to quantify association between random vectors. Most existing nonparametric esti…
On minimum Bregman divergence inference
Soumik Purkayastha, Ayanendranath Basu
In this paper a new family of minimum divergence estimators based on the Bregman divergence is proposed. The popular density power divergence (DPD) class of estimators is a sub-cla…