5 citations · 7 across the 9 of their papers we have counts for
4 papers · 1 filter
Tangent Space Causal Inference: Leveraging Vector Fields for Causal Discovery in Dynamical Systems
Kurt Butler, Daniel Waxman, Petar M. Djurić
Causal discovery with time series data remains a challenging yet increasingly important task across many scientific domains. Convergent cross mapping (CCM) and related methods have…
On Counterfactual Interventions in Vector Autoregressive Models
Kurt Butler, Marija Iloska, Petar M. Djuric
Counterfactual reasoning allows us to explore hypothetical scenarios in order to explain the impacts of our decisions. However, addressing such inquires is impossible without estab…
Explainable Learning with Gaussian Processes
Kurt Butler, Guanchao Feng, Petar M. Djuric
The field of explainable artificial intelligence (XAI) attempts to develop methods that provide insight into how complicated machine learning methods make predictions. Many methods…
Dagma-DCE: Interpretable, Non-Parametric Differentiable Causal Discovery
Daniel Waxman, Kurt Butler, Petar M. Djuric
We introduce Dagma-DCE, an interpretable and model-agnostic scheme for differentiable causal discovery. Current non- or over-parametric methods in differentiable causal discovery u…