4 citations · 4 across the 5 of their papers we have counts for
5 papers · 1 filter
LUCID: Learning Under Confounding for Inference and Discovery in Time Series
Mohammad Fesanghary
Unobserved common causes are pervasive in real-world time series and can induce spurious associations that causal discovery methods mistake for direct edges. We propose LUCID (Lear…
Causal-TS: A Python Library for Causal Discovery in High-Dimensional and Nonstationary Time Series
Mohammad Fesanghary
We describe Causal-TS, an open-source Python library for causal discovery in high-dimensional and nonstationary multivariate time series. Causal-TS provides four specialized algori…
CEDAR: Causal Edge Discovery for Autoregressive Processes
Mohammad Fesanghary
We propose CEDAR (Causal Edge Discovery for Autoregressive Processes), a constraint-based method for lagged causal edge discovery in sparse autoregressive time series. CEDAR screen…
GRACE: Gated Refinement for Accurate Causal Edge Discovery in High-Dimensional Time Series
Mohammad Fesanghary, Abhinav Havaldar
From climate teleconnections to gene regulation, modern time-series datasets encompass tens or hundreds of interacting variables, making causal discovery increasingly challenging.…
Efficient Causal Discovery for Autoregressive Time Series
Mohammad Fesanghary, Achintya Gopal
In this study, we present a novel constraint-based algorithm for causal structure learning specifically designed for nonlinear autoregressive time series. Our algorithm significant…