33 citations · 97 across the 11 of their papers we have counts for
12 papers
Tuning Derivatives for Causal Fairness in Machine Learning
Filip Edström, Guilherme W. F. Barros, Tetiana Gorbach +1
Artificial-intelligence systems are becoming ubiquitous in society, yet their predictions typically inherit biases with respect to protected attributes such as race, gender, or age…
Complementary strengths of the Neyman-Rubin and graphical causal frameworks
Tetiana Gorbach, Xavier de Luna, Juha Karvanen +1
This article contributes to the discussion on the relationship between the Neyman-Rubin and the graphical frameworks for causal inference. We present specific examples of data-gene…
Valid causal inference with unobserved confounding in high-dimensional settings
Niloofar Moosavi, Tetiana Gorbach, Xavier de Luna
Various methods have recently been proposed to estimate causal effects with confidence intervals that are uniformly valid over a set of data generating processes when high-dimensio…
Causal inference with a functional outcome
Kreske Ecker, Xavier de Luna, Lina Schelin
This paper presents methods to study the causal effect of a binary treatment on a functional outcome with observational data. We define a Functional Average Treatment Effect and de…
Contrasting Identifying Assumptions of Average Causal Effects: Robustness and Semiparametric Efficiency
Tetiana Gorbach, Xavier de Luna, Juha Karvanen +1
Semiparametric inference on average causal effects from observational data is based on assumptions yielding identification of the effects. In practice, several distinct identifying…
The costs and benefits of uniformly valid causal inference with high-dimensional nuisance parameters
Niloofar Moosavi, Jenny Häggström, Xavier de Luna
Important advances have recently been achieved in developing procedures yielding uniformly valid inference for a low dimensional causal parameter when high-dimensional nuisance mod…
Covariate balancing for causal inference on categorical and continuous treatments
Seong-ho Lee, Yanyuan Ma, Xavier de Luna
We propose novel estimators for categorical and continuous treatments by using an optimal covariate balancing strategy for inverse probability weighting. The resulting estimators a…
Causal inference taking into account unobserved confounding
Minna Genbäck, Xavier de Luna
Causal inference with observational data can be performed under an assumption of no unobserved confounders (unconfoundedness assumption). There is, however, seldom clear subject-ma…
Sensitivity analysis for unobserved confounding of direct and indirect effects using uncertainty intervals
Anita Lindmark, Xavier de Luna, Marie Eriksson
To estimate direct and indirect effects of an exposure on an outcome from observed data strong assumptions about unconfoundedness are required. Since these assumptions cannot be te…
Inference for partial correlation when data are missing not at random
Tetiana Gorbach, Xavier de Luna
We introduce uncertainty regions to perform inference on partial correlations when data are missing not at random. These uncertainty regions are shown to have a desired asymptotic…
Data-driven Algorithms for Dimension Reduction in Causal Inference
Emma Persson, Jenny Häggström, Ingeborg Waernbaum +1
In observational studies, the causal effect of a treatment may be confounded with variables that are related to both the treatment and the outcome of interest. In order to identify…
Targeted smoothing parameter selection for estimating average causal effects
Jenny Häggström, Xavier de Luna
The non-parametric estimation of average causal effects in observational studies often relies on controlling for confounding covariates through smoothing regression methods such as…