Graph Neural Networks for Treatment Effect Prediction
G Panagopoulos, D Malitesta, FD Malliaros… - arXiv preprint arXiv …, 2024 - arxiv.org
Estimating causal effects in e-commerce tends to involve costly treatment assignments
which can be impractical in large-scale settings. Leveraging machine learning to predict …
which can be impractical in large-scale settings. Leveraging machine learning to predict …
Neural Networks with Causal Graph Constraints: A New Approach for Treatment Effects Estimation
R Pros, J Vitrià - arXiv preprint arXiv:2404.12238, 2024 - arxiv.org
In recent years, there has been a growing interest in using machine learning techniques for
the estimation of treatment effects. Most of the best-performing methods rely on …
the estimation of treatment effects. Most of the best-performing methods rely on …
Uplift Modeling Under Limited Supervision
Estimating causal effects in e-commerce tends to involve costly treatment assignments
which can be impractical in large-scale settings. Leveraging machine learning to predict …
which can be impractical in large-scale settings. Leveraging machine learning to predict …
Treatment Effect Estimation Under Unknown Interference
Causal inference is a powerful tool for effective decision-making in various areas, such as
medicine and commerce. For example, it allows businesses to determine whether an …
medicine and commerce. For example, it allows businesses to determine whether an …