1 citations · 1 across the 9 of their papers we have counts for
4 papers · 1 filter
Epidemiological Causal Graph Identification: Challenges, Identifiability and Algorithms
Sambit Mishra, Yingying Wang, Christine K. Johnson +1
Causal discovery from observational data is fundamental to statistics and machine learning, yet determining causal direction without interventions necessitates structural assumptio…
On the Identifiability of Mixed Ordinal and Exponential Family Causal DAGs under Linear Parametric Models
Sambit Mishra, Urbashi Mitra
The problem of identifiability in linear parametric models (LPMs) whose nodes follow either an ordered logit model or a regular one-parameter exponential family is evaluated. The r…
Causal Discovery in Equal Variance Linear Gaussian DAGs via SURE-Tuned Ridge Regression
Sambit Mishra, Urbashi Mitra
Recovering the directed acyclic graph (DAG) of a structural equation model (SEM) from observational data is a central problem in causal discovery. The iterative gradient descent an…
Atomic Hybrid Sparse/Diffuse Channel Estimation and Cramér-Rao Bounds Analysis
Lei Lyu, Maxime Ferreira Da Costa, Urbashi Mitra
In this paper, an atomic hybrid sparse/diffuse (aHSD) channel model in the frequency domain is proposed. Based on a structural analysis of the resolvable paths and diffuse scatteri…