3 citations · 9 across the 8 of their papers we have counts for
8 papers
Sensitivity Analysis to Unobserved Confounding with Copula-based Normalizing Flows
Sourabh Balgi, Marc Braun, Jose M. Peña +1
We propose a novel method for sensitivity analysis to unobserved confounding in causal inference. The method builds on a copula-based causal graphical normalizing flow that we term…
Deep Learning With DAGs
Sourabh Balgi, Adel Daoud, Jose M. Peña +2
Social science theories often postulate causal relationships among a set of variables or events. Although directed acyclic graphs (DAGs) are increasingly used to represent these th…
Navigating Unmeasured Confounding in Quantitative Sociology: A Sensitivity Framework
Cheng Lin, Jose M. Pena, Adel Daoud
Unmeasured confounding remains a critical challenge in causal inference for the social sciences. This paper proposes a sensitivity analysis framework to systematically evaluate how…
-GNF: A Copula-based Sensitivity Analysis to Unobserved Confounding Using Normalizing Flows
Sourabh Balgi, Jose M. Peña, Adel Daoud
We propose a novel sensitivity analysis to unobserved confounding in observational studies using copulas and normalizing flows. Using the idea of interventional equivalence of stru…
Counterfactual Analysis of the Impact of the IMF Program on Child Poverty in the Global-South Region using Causal-Graphical Normalizing Flows
Sourabh Balgi, Jose M. Peña, Adel Daoud
This work demonstrates the application of a particular branch of causal inference and deep learning models: \emph{causal-Graphical Normalizing Flows (c-GNFs)}. In a recent contribu…
Personalized Public Policy Analysis in Social Sciences using Causal-Graphical Normalizing Flows
Sourabh Balgi, Jose M. Pena, Adel Daoud
Structural Equation/Causal Models (SEMs/SCMs) are widely used in epidemiology and social sciences to identify and analyze the average causal effect (ACE) and conditional ACE (CACE)…
Contradistinguisher: A Vapnik's Imperative to Unsupervised Domain Adaptation
Sourabh Balgi, Ambedkar Dukkipati
A complex combination of simultaneous supervised-unsupervised learning is believed to be the key to humans performing tasks seamlessly across multiple domains or tasks. This phenom…
CUDA: Contradistinguisher for Unsupervised Domain Adaptation
Sourabh Balgi, Ambedkar Dukkipati
In this paper, we propose a simple model referred as Contradistinguisher (CTDR) for unsupervised domain adaptation whose objective is to jointly learn to contradistinguish on unlab…