17 citations · 49 across the 10 of their papers we have counts for
10 papers · 1 filter
MissDeepCausal: Causal Inference from Incomplete Data Using Deep Latent Variable Models
Imke Mayer, Julie Josse, Félix Raimundo +1
Inferring causal effects of a treatment, intervention or policy from observational data is central to many applications. However, state-of-the-art methods for causal inference seld…
Doubly robust treatment effect estimation with missing attributes
Imke Mayer, Erik Sverdrup, Tobias Gauss +3
Missing attributes are ubiquitous in causal inference, as they are in most applied statistical work. In this paper, we consider various sets of assumptions under which causal infer…
Adaptive Bayesian SLOPE -- High-dimensional Model Selection with Missing Values
Wei Jiang, Malgorzata Bogdan, Julie Josse +3
We consider the problem of variable selection in high-dimensional settings with missing observations among the covariates. To address this relatively understudied problem, we propo…
Main effects and interactions in mixed and incomplete data frames
Geneviève Robin, Olga Klopp, Julie Josse +2
A mixed data frame (MDF) is a table collecting categorical, numerical and count observations. The use of MDF is widespread in statistics and the applications are numerous from abun…
Empirical Bayes approaches to PageRank type algorithms for rating scientific journals
Jean-Louis Foulley, Gilles Celeux, Julie Josse
Following criticisms against the journal Impact Factor, new journal influence scores have been developed such as the Eigenfactor or the Prestige Scimago Journal Rank. They are base…
Some discussions on the Read Paper "Beyond subjective and objective in statistics" by A. Gelman and C. Hennig
Gilles Celeux, Jack Jewson, Julie Josse +2
This note is a collection of several discussions of the paper "Beyond subjective and objective in statistics", read by A. Gelman and C. Hennig to the Royal Statistical Society on A…