Causal random forests provide efficient estimates of heterogeneous treat...
Conformal prediction is a theoretically grounded framework for construct...
There are many measures to report so-called treatment or causal effect:
...
An individualized treatment regime (ITR) is a decision rule that assigns...
The limited scope of Randomized Controlled Trials (RCT) is increasingly ...
BACKGROUND: As databases grow larger, it becomes harder to fully control...
Uncertainty quantification of predictive models is crucial in decision-m...
In recent decades, technological advances have made it possible to colle...
How to learn a good predictor on data with missing values? Most efforts ...
While a randomized controlled trial (RCT) readily measures the average
t...
The simultaneous availability of experimental and observational data to
...
With increasing data availability, treatment causal effects can be evalu...
VARCLUST algorithm is proposed for clustering variables under the assump...
The presence of missing values makes supervised learning much more
chall...
We propose Robust Lasso-Zero, an extension of the Lasso-Zero methodology...
Inferring causal effects of a treatment, intervention or policy from
obs...
A major caveat of large scale data is their incom-pleteness. We propose ...
Missing data is a crucial issue when applying machine learning algorithm...
We consider building predictors when the data have missing values. We st...
Missing attributes are ubiquitous in causal inference, as they are in mo...
The selection of variables with high-dimensional and missing data is a m...
Missing values are unavoidable when working with data. Their occurrence ...
Missing Not At Random values are considered to be non-ignorable and requ...
Matrix completion based on low-rank models is very popular and comes wit...
In many application settings, the data are plagued with missing features...
Missing values challenge data analysis because many supervised and
unsu-...
Many applications of machine learning involve the analysis of large data...
A mixed data frame (MDF) is a table collecting categorical, numerical an...
Logistic regression is a common classification method in supervised lear...
Statistical analysis of large data sets offers new opportunities to bett...
We develop a flexible framework for low-rank matrix estimation that allo...