3 papers
stat.ML2026
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.…
stat.ME2025
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…
stat.ME2025
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…