2 citations · 2 across the 4 of their papers we have counts for
4 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…
Flow IV: Counterfactual Inference In Nonseparable Outcome Models Using Instrumental Variables
Marc Braun, Jose M. Peña, Adel Daoud
To reach human level intelligence, learning algorithms need to incorporate causal reasoning. But identifying causality, and particularly counterfactual reasoning, remains elusive.…
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…