5 papers
Group Interventions on Deep Networks for Causal Discovery in Subsystems
Wasim Ahmad, Joachim Denzler, Maha Shadaydeh
Causal discovery uncovers complex relationships between variables, enhancing predictions, decision-making, and insights into real-world systems, especially in nonlinear multivariat…
Regime Identification for Improving Causal Analysis in Non-stationary Timeseries
Wasim Ahmad, Maha Shadaydeh, Joachim Denzler
Time series data from real-world systems often display non-stationary behavior, indicating varying statistical characteristics over time. This inherent variability poses significan…
Deep Learning-based Group Causal Inference in Multivariate Time-series
Wasim Ahmad, Maha Shadaydeh, Joachim Denzler
Causal inference in a nonlinear system of multivariate timeseries is instrumental in disentangling the intricate web of relationships among variables, enabling us to make more accu…
Causal Discovery using Model Invariance through Knockoff Interventions
Wasim Ahmad, Maha Shadaydeh, Joachim Denzler
Cause-effect analysis is crucial to understand the underlying mechanism of a system. We propose to exploit model invariance through interventions on the predictors to infer causali…
Causal Inference in Non-linear Time-series using Deep Networks and Knockoff Counterfactuals
Wasim Ahmad, Maha Shadaydeh, Joachim Denzler
Estimating causal relations is vital in understanding the complex interactions in multivariate time series. Non-linear coupling of variables is one of the major challenges inaccura…