Search Results for author:
Found 23 papers, 18 papers with code
MMD-based Variable Importance for Distributional Random Forest
Distributional Random Forest (DRF) is a flexible forest-based method to estimate the full conditional distribution of a multivariate output of interest given input variables.
Positivity-free Policy Learning with Observational Data
Policy learning utilizing observational data is pivotal across various domains, with the objective of learning the optimal treatment assignment policy while adhering to specific constraints such as fairness, budget, and simplicity.
Variable importance for causal forests: breaking down the heterogeneity of treatment effects
In this article, we develop a new importance variable algorithm for causal forests, to quantify the impact of each input on the heterogeneity of treatment effects.
Conformal Prediction with Missing Values
This motivates our novel generalized conformalized quantile regression framework, missing data augmentation, which yields prediction intervals that are valid conditionally to the patterns of missing values, despite their exponential number.
Efficient and robust transfer learning of optimal individualized treatment regimes with right-censored survival data
We present an efficient and robust transfer learning framework for estimating the optimal ITR with right-censored survival data that generalizes well to the target population.
Benchmarking missing-values approaches for predictive models on health databases
Using gradient-boosted trees, we compare native support for missing values with simple and state-of-the-art imputation prior to learning.
Adaptive Conformal Predictions for Time Series
While recent works tackled this issue, we argue that Adaptive Conformal Inference (ACI, Gibbs and Cand{\`e}s, 2021), developed for distribution-shift time series, is a good procedure for time series with general dependency.
Model-based Clustering with Missing Not At Random Data
In this paper, we propose model-based clustering algorithms designed to handle very general types of missing data, including MNAR data.
What’s a good imputation to predict with missing values?
In fact, we show that on perfectly imputed data the best regression function will generally be discontinuous, which makes it hard to learn.
What's a good imputation to predict with missing values?
In fact, we show that on perfectly imputed data the best regression function will generally be discontinuous, which makes it hard to learn.
NeuMiss networks: differentiable programming for supervised learning with missing values
We provide an upper bound on the Bayes risk of NeuMiss networks, and show that they have good predictive accuracy with both a number of parameters and a computational complexity independent of the number of missing data patterns.
Robust Lasso-Zero for sparse corruption and model selection with missing covariates
The use of Robust Lasso-Zero is showcased for variable selection with missing values in the covariates.
MissDeepCausal: Causal Inference from Incomplete Data Using Deep Latent Variable Models
Inferring causal effects of a treatment, intervention or policy from observational data is central to many applications.
Missing Data Imputation using Optimal Transport
Missing data is a crucial issue when applying machine learning algorithms to real-world datasets.
Linear predictor on linearly-generated data with missing values: non consistency and solutions
In the particular Gaussian case, it can be written as a linear function of multiway interactions between the observed data and the various missing-value indicators.
Doubly robust treatment effect estimation with missing attributes
We find, however, that doubly robust modifications of standard methods for average treatment effect estimation with missing data repeatedly perform better than their non-doubly robust baselines; for example, doubly robust generalized propensity score methods beat inverse-weighting with the generalized propensity score.
Methodology 93C41, 62G35, 62F35, 62P10
Adaptive Bayesian SLOPE -- High-dimensional Model Selection with Missing Values
We consider the problem of variable selection in high-dimensional settings with missing observations among the covariates.
Methodology Applications Computation
Estimation and imputation in Probabilistic Principal Component Analysis with Missing Not At Random data
Considering a data matrix generated from a probabilistic principal component analysis (PPCA) model containing several MNAR variables, not necessarily under the same self-masked missing mechanism, we propose estimators for the means, variances and covariances of the variables and study their consistency.
Statistics Theory Statistics Theory
On the consistency of supervised learning with missing values
A striking result is that the widely-used method of imputing with a constant, such as the mean prior to learning is consistent when missing values are not informative.
Imputation and low-rank estimation with Missing Not At Random data
Our second contribution is to suggest a computationally efficient surrogate estimation by implicitly taking into account the joint distribution of the data and the missing mechanism: the data matrix is concatenated with the mask coding for the missing values; a low-rank structure for exponential family is assumed on this new matrix, in order to encode links between variables and missing mechanisms.
Low-rank Interaction with Sparse Additive Effects Model for Large Data Frames
In this paper, we introduce a low-rank interaction and sparse additive effects (LORIS) model which combines matrix regression on a dictionary and low-rank design, to estimate main effects and interactions simultaneously.
Stochastic Approximation EM for Logistic Regression with Missing Values
We propose a complete approach, including the estimation of parameters and their variance, derivation of confidence intervals, a model selection procedure, and a method for prediction on test sets with missing values.
Methodology
Bootstrap-Based Regularization for Low-Rank Matrix Estimation
In the simplest case, with an isotropic noise model, our method is equivalent to a classical singular value shrinkage estimator.
