collaborators

13 papers

stat.ME2026

A Sensitivity Analysis Framework for Causal Inference Under Interference

Matvey Ortyashov, AmirEmad Ghassami

In many applications of causal inference, the treatment received by one unit may influence the outcome of another, a phenomenon referred to as interference. Although there are seve…

math.ST2026

Multiply Robust Causal Mediation Analysis with Continuous Treatments

Yizhen Xu, AmirEmad Ghassami, Numair Sani +1

In many applications, researchers are interested in the direct and indirect causal effects of a treatment or exposure on an outcome of interest. Mediation analysis offers a rigorou…

stat.ME2026

MediEncoder: Nonlinear Representation Learning for High-Dimensional Causal Mediation Analysis

Shi Bo, Debarghya Mukherjee, AmirEmad Ghassami

Causal mediation analysis decomposes a treatment effect into indirect pathways through mediators and direct pathways not operating through them. Modern biomedical studies often inv…

stat.ME2026

Causal Discovery in Structural VAR Models Under Equal Noise Variance

SeyedSina Seyedi HasanAbadi, Fahimeh Arab, Erfan Nozari +1

Causal discovery from multivariate time series is challenging when causal effects may occur both across time and within the same sampling interval. This issue is especially importa…

stat.ME2026

Model-Robust Direct Effect Under Confounder-Mediator Ambiguity

AmirEmad Ghassami

Direct effect analyses usually require deciding whether a focal variable is a pre-exposure confounder or a post-exposure mediator. In observational studies, that distinction may be…

cs.LG2026

Causal Discovery in Linear Models with Unobserved Variables and Measurement Error

Yuqin Yang, Mohamed Nafea, Negar Kiyavash +2

The presence of unobserved common causes and measurement error poses two major obstacles to causal structure learning, since ignoring either source of complexity can induce spuriou…