Estimating the effects of treatments with an associated dose on an insta...
This paper addresses unsupervised representation learning on tabular dat...
Causality has the potential to truly transform the way we solve a large
...
Causal deep learning (CDL) is a new and important research area in the l...
We are interested in unsupervised structure learning with a particular f...
Estimating heterogeneous treatment effects is an important problem acros...
Missing data is a systemic problem in practical scenarios that causes no...
Choosing the best treatment-plan for each individual patient requires
ac...
Machine learning models have been criticized for reflecting unfair biase...
Conditional average treatment effects (CATEs) allow us to understand the...
Multi-task learning (MTL) can improve performance on a task by sharing
r...
Classification is a well-studied machine learning task which concerns th...
In the majority of executive domains, a notion of normality is involved ...
Applying causal inference models in areas such as economics, healthcare ...
Uplift modeling requires experimental data, preferably collected in rand...