Stefan Feuerriegel

Deconfounding Temporal Autoencoder: Estimating Treatment Effects over Time Using Noisy Proxies

2021 · arXiv (Cornell University)

Estimating individualized treatment effects (ITEs) from observational data is crucial for decision-making. In order to obtain unbiased ITE estimates, a common assumption is that all confounders are observed. However, in practice, it is unlikely that …

QA Domain Adaptation using Hidden Space Augmentation and Self-Supervised Contrastive Adaptation

2022 · arXiv (Cornell University)

Question answering (QA) has recently shown impressive results for answering questions from customized domains. Yet, a common challenge is to adapt QA models to an unseen target domain. In this paper, we propose a novel …

Learning Representations of Instruments for Partial Identification of Treatment Effects

2024 · arXiv (Cornell University)

Reliable estimation of treatment effects from observational data is important in many disciplines such as medicine. However, estimation is challenging when unconfoundedness as a standard assumption in the causal inference literature is violated. In this …