6 citations · 8 across the 4 of their papers we have counts for
6 papers
Treatment Effect Estimation in Causal Survival Analysis: Practical Recommendations
Charlotte Voinot, Clément Berenfeld, Imke Mayer +2
The restricted mean survival time (RMST) difference offers an interpretable causal contrast to estimate the treatment effect for time-to-event outcomes, yet a wide range of availab…
Generalizing treatment effects with incomplete covariates: identifying assumptions and multiple imputation algorithms
Imke Mayer, Julie Josse, Traumabase Group
We focus on the problem of generalizing a causal effect estimated on a randomized controlled trial (RCT) to a target population described by a set of covariates from observational…
Causal inference methods for combining randomized trials and observational studies: a review
Bénédicte Colnet, Imke Mayer, Guanhua Chen +6
With increasing data availability, causal effects can be evaluated across different data sets, both randomized controlled trials (RCTs) and observational studies. RCTs isolate the…
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…
R-miss-tastic: a unified platform for missing values methods and workflows
Imke Mayer, Aude Sportisse, Julie Josse +2
Missing values are unavoidable when working with data. Their occurrence is exacerbated as more data from different sources become available. However, most statistical models and vi…