activity
20102024
most citedCombinatorial Optimization by Learning and Simulation of Bayesian Networks

116 citations · 267 across the 30 of their papers we have counts for

collaborators
Showing stat.MEShow all

6 papers · 1 filter

stat.ME2023

Bounding the Probabilities of Benefit and Harm Through Sensitivity Parameters and Proxies

Jose M. Peña

We present two methods for bounding the probabilities of benefit and harm under unmeasured confounding. The first method computes the (upper or lower) bound of either probability a…

stat.ME2022★ 1 cited

-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…

stat.ME2021

Simple yet Sharp Sensitivity Analysis for Unmeasured Confounding

Jose M. Peña

We present a method for assessing the sensitivity of the true causal effect to unmeasured confounding. The method requires the analyst to set two intuitive parameters. Otherwise, t…

stat.ME2021

On the Non-Monotonicity of a Non-Differentially Mismeasured Binary Confounder

Jose M. Peña

Suppose that we are interested in the average causal effect of a binary treatment on an outcome when this relationship is confounded by a binary confounder. Suppose that the confou…

stat.ME2020

On the Monotonicity of a Nondifferentially Mismeasured Binary Confounder

Jose M. Peña

Suppose that we are interested in the average causal effect of a binary treatment on an outcome when this relationship is confounded by a binary confounder. Suppose that the confou…

stat.ME2020

Factorization of the Partial Covariance in Singly-Connected Path Diagrams

Jose M. Peña

We extend path analysis by showing that, for a singly-connected path diagram, the partial covariance of two random variables factorizes over the nodes and edges in the path between…