Causal inference has numerous real-world applications in many domains, such as health
care, marketing, political science, and online advertising. Treatment effect estimation, a …

The need to evaluate treatment effectiveness is ubiquitous in most of empirical science, and
interest in flexibly investigating effect heterogeneity is growing rapidly. To do so, a multitude …

Effectively representing users lie at the core of modern recommender systems. Since users'
interests naturally exhibit multiple aspects, it is of increasing interest to develop multi-interest …

Instrumental variables (IVs), sources of treatment randomization that are conditionally
independent of the outcome, play an important role in causal inference with unobserved …

This invited Review discusses causal learning in the context of robotic intelligence. The
Review introduces the psychological findings on causal learning in human cognition, as well …

Estimating individual treatment effect (ITE) from observational data is a crucial but
challenging task. Disentangled representations have been used to separate proxy variables …

In social sciences and economics, causal inference traditionally focuses on assessing the
impact of predefined treatments (or interventions) on predefined outcomes, such as the …

The advancements in disentangled representation learning significantly enhance the
accuracy of counterfactual predictions by granting precise control over instrumental …

This study introduces a data-driven, machine learning-based method to detect suitable
control variables and instruments for assessing the causal effect of a treatment on an …

Adjusting for latent covariates is crucial for estimating causal effects from observational
textual data. Most existing methods only account for confounding covariates that affect both …