6 citations · 8 across the 9 of their papers we have counts for
6 papers · 1 filter
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…
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…