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08/07/2023
Variable importance for causal forests: breaking down the heterogeneity of treatment effects
Causal random forests provide efficient estimates of heterogeneous treat...
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06/05/2023
Conformal Prediction with Missing Values
Conformal prediction is a theoretically grounded framework for construct...
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03/28/2023
Risk ratio, odds ratio, risk difference... Which causal measure is easier to generalize?
There are many measures to report so-called treatment or causal effect: ...
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01/13/2023
Efficient and robust transfer learning of optimal individualized treatment regimes with right-censored survival data
An individualized treatment regime (ITR) is a decision rule that assigns...
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08/16/2022
Reweighting the RCT for generalization: finite sample analysis and variable selection
The limited scope of Randomized Controlled Trials (RCT) is increasingly ...
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02/17/2022
Benchmarking missing-values approaches for predictive models on health databases
BACKGROUND: As databases grow larger, it becomes harder to fully control...
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02/15/2022
Adaptive Conformal Predictions for Time Series
Uncertainty quantification of predictive models is crucial in decision-m...
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12/20/2021
Model-based Clustering with Missing Not At Random Data
In recent decades, technological advances have made it possible to colle...
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06/01/2021
What's a good imputation to predict with missing values?
How to learn a good predictor on data with missing values? Most efforts ...
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05/13/2021
Generalizing a causal effect: sensitivity analysis and missing covariates
While a randomized controlled trial (RCT) readily measures the average t...
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04/26/2021
Transporting treatment effects with incomplete attributes
The simultaneous availability of experimental and observational data to ...
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11/16/2020
Causal inference methods for combining randomized trials and observational studies: a review
With increasing data availability, treatment causal effects can be evalu...
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11/12/2020
VARCLUST: clustering variables using dimensionality reduction
VARCLUST algorithm is proposed for clustering variables under the assump...
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07/03/2020
Neumann networks: differential programming for supervised learning with missing values
The presence of missing values makes supervised learning much more chall...
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05/12/2020
Robust Lasso-Zero for sparse corruption and model selection with missing covariates
We propose Robust Lasso-Zero, an extension of the Lasso-Zero methodology...
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02/25/2020
MissDeepCausal: Causal Inference from Incomplete Data Using Deep Latent Variable Models
Inferring causal effects of a treatment, intervention or policy from obs...
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02/21/2020
Debiasing Stochastic Gradient Descent to handle missing values
A major caveat of large scale data is their incom-pleteness. We propose ...
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02/10/2020
Missing Data Imputation using Optimal Transport
Missing data is a crucial issue when applying machine learning algorithm...
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02/03/2020
Linear predictor on linearly-generated data with missing values: non consistency and solutions
We consider building predictors when the data have missing values. We st...
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10/23/2019
Doubly robust treatment effect estimation with missing attributes
Missing attributes are ubiquitous in causal inference, as they are in mo...
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09/14/2019
Adaptive Bayesian SLOPE – High-dimensional Model Selection with Missing Values
The selection of variables with high-dimensional and missing data is a m...
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08/13/2019
R-miss-tastic: a unified platform for missing values methods and workflows
Missing values are unavoidable when working with data. Their occurrence ...
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06/06/2019
Estimation and imputation in Probabilistic Principal Component Analysis with Missing Not At Random data
Missing Not At Random values are considered to be non-ignorable and requ...
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06/06/2019
Estimation with informative missing data in the low-rank model with random effects
Matrix completion based on low-rank models is very popular and comes wit...
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02/19/2019
On the consistency of supervised learning with missing values
In many application settings, the data are plagued with missing features...
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12/29/2018
Imputation and low-rank estimation with Missing Non At Random data
Missing values challenge data analysis because many supervised and unsu-...
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12/20/2018
Low-rank Interaction with Sparse Additive Effects Model for Large Data Frames
Many applications of machine learning involve the analysis of large data...
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06/26/2018
Main effects and interactions in mixed and incomplete data frames
A mixed data frame (MDF) is a table collecting categorical, numerical an...
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05/11/2018
Stochastic Approximation EM for Logistic Regression with Missing Values
Logistic regression is a common classification method in supervised lear...
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04/30/2018
Imputation of mixed data with multilevel singular value decomposition
Statistical analysis of large data sets offers new opportunities to bett...
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10/30/2014
