6 papers
Granger Causality in Extremes
Juraj Bodik, Olivier C. Pasche
We introduce a rigorous mathematical framework for Granger causality in extremes, designed to identify causal links from extreme events in time series. Granger causality plays a pi…
CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk
Ilia Azizi, Juraj Bodik, Jakob Heiss +1
Accurate uncertainty quantification is critical for reliable predictive modeling. Existing methods typically address either aleatoric uncertainty due to measurement noise or episte…
Retrospective Counterfactual Prediction by Conditioning on the Factual Outcome: A Cross-World Approach
Juraj Bodik
Retrospective causal questions ask what would have happened to an observed individual had they received a different treatment. We study the problem of estimating $μ(x,y)=\mathbb{E…
Cross-World Assumption and Refining Prediction Intervals for Individual Treatment Effects
Juraj Bodik, Yaxuan Huang, Bin Yu
While average treatment effects (ATE) and conditional average treatment effects (CATE) provide valuable population- and subgroup-level summaries, they fail to capture uncertainty a…
Identifiability of causal graphs under nonadditive conditionally parametric causal models
Juraj Bodik, Valérie Chavez-Demoulin
Existing approaches to causal discovery often rely on restrictive modeling assumptions that limit their applicability in real-world settings, particularly when data are heavy-taile…
Structural restrictions in local causal discovery: identifying direct causes of a target variable
Juraj Bodik, Valérie Chavez-Demoulin
We consider the problem of learning a set of direct causes of a target variable from an observational joint distribution. Learning directed acyclic graphs (DAGs) that represent the…