60 citations · 306 across the 40 of their papers we have counts for
4 papers · 1 filter
Model-based Clustering with Missing Not At Random Data
Aude Sportisse, Matthieu Marbac, Fabien Laporte +4
Model-based unsupervised learning, as any learning task, stalls as soon as missing data occurs. This is even more true when the missing data are informative, or said missing not at…
What's a good imputation to predict with missing values?
Marine Le Morvan, Julie Josse, Erwan Scornet +1
How to learn a good predictor on data with missing values? Most efforts focus on first imputing as well as possible and second learning on the completed data to predict the outcome…
Causal effect on a target population: a sensitivity analysis to handle missing covariates
Bénédicte Colnet, Julie Josse, Erwan Scornet +1
Randomized Controlled Trials (RCTs) are often considered the gold standard for estimating causal effect, but they may lack external validity when the population eligible to the RCT…
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…