6 citations · 8 across the 9 of their papers we have counts for
9 papers
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…
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…
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…
Extreme Treatment Effect: Extrapolating Dose-Response Function Into Extreme Treatment Domain
Juraj Bodik
The potential outcomes framework serves as a fundamental tool for quantifying causal effects. The average dose-response function (also called the effect curve), denoted as (μ(t)),…
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…
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…
Detecting causal covariates for extreme dependence structures
Juraj Bodik, Linda Mhalla, Valérie Chavez-Demoulin
Determining the causes of extreme events is a fundamental question in many scientific fields. An important aspect when modelling multivariate extremes is the tail dependence. In ap…
Causality in extremes of time series
Juraj Bodik, Zbyněk Pawlas, Milan Paluš
Consider two stationary time series with heavy-tailed marginal distributions. We aim to detect whether they have a causal relation, that is, if a change in one causes a change in t…