Numerous deep learning architectures have been developed to accommodate the diversity
of time-series datasets across different domains. In this article, we survey common encoder …

Causal inference is a critical research topic across many domains, such as statistics,
computer science, education, public policy, and economics, for decades. Nowadays …

The two fields of machine learning and graphical causality arose and are developed
separately. However, there is, now, cross-pollination and increasing interest in both fields to …

Recent VQA models may tend to rely on language bias as a shortcut and thus fail to
sufficiently learn the multi-modal knowledge from both vision and language. In this paper …

We propose a novel method for imputing missing data by adapting the well-known
Generative Adversarial Nets (GAN) framework. Accordingly, we call our method Generative …

Estimating counterfactual outcomes over time from observational data is relevant for many
applications (eg, personalized medicine). Yet, state-of-the-art methods build upon simple …

Causal machine learning (CML) has experienced increasing popularity in healthcare.
Beyond the inherent capabilities of adding domain knowledge into learning systems, CML …

This paper addresses the use of neural networks for the estimation of treatment effects from
observational data. Generally, estimation proceeds in two stages. First, we fit models for the …

Estimating individual treatment effects from observational data is challenging due to
treatment selection bias. Prevalent methods mainly mitigate this issue by aligning different …

The COVID-19 global pandemic is a threat not only to the health of millions of individuals,
but also to the stability of infrastructure and economies around the world. The disease will …