599 citations · 862 across the 35 of their papers we have counts for
5 papers · 1 filter
Quantifying Treatment Effects: Estimating Risk Ratios in Causal Inference
Ahmed Boughdiri, Julie Josse, Erwan Scornet
Randomized Controlled Trials (RCT) are the current gold standards to empirically measure the effect of a new drug. However, they may be of limited size and resorting to complementa…
A primer on linear classification with missing data
Angel D Reyero Lobo, Alexis Ayme, Claire Boyer +1
Supervised learning with missing data aims at building the best prediction of a target output based on partially-observed inputs. Major approaches to address this problem can be de…
What Is a Good Imputation Under MAR Missingness?
Jeffrey Näf, Erwan Scornet, Julie Josse
Missing values pose a persistent challenge in modern data science. Consequently, there is an ever-growing number of publications introducing new imputation methods in various field…
Random features models: a way to study the success of naive imputation
Alexis Ayme, Claire Boyer, Aymeric Dieuleveut +1
Constant (naive) imputation is still widely used in practice as this is a first easy-to-use technique to deal with missing data. Yet, this simple method could be expected to induce…
Do we need rebalancing strategies? A theoretical and empirical study around SMOTE and its variants
Abdoulaye Sakho, Emmanuel Malherbe, Erwan Scornet
Synthetic Minority Oversampling Technique (SMOTE) is a common rebalancing strategy for handling imbalanced tabular data sets. However, few works analyze SMOTE theoretically. In thi…