Causal transformer for estimating counterfactual outcomes
V Melnychuk, D Frauen… - … Conference on Machine …, 2022 - proceedings.mlr.press
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 …
applications (eg, personalized medicine). Yet, state-of-the-art methods build upon simple …
Transfer learning on heterogeneous feature spaces for treatment effects estimation
I Bica, M van der Schaar - Advances in Neural Information …, 2022 - proceedings.neurips.cc
Consider the problem of improving the estimation of conditional average treatment effects
(CATE) for a target domain of interest by leveraging related information from a source …
(CATE) for a target domain of interest by leveraging related information from a source …
Causal deep learning
Causality has the potential to truly transform the way we solve a large number of real-world
problems. Yet, so far, its potential largely remains to be unlocked as causality often requires …
problems. Yet, so far, its potential largely remains to be unlocked as causality often requires …
Accounting for informative sampling when learning to forecast treatment outcomes over time
T Vanderschueren, A Curth… - International …, 2023 - proceedings.mlr.press
Abstract Machine learning (ML) holds great potential for accurately forecasting treatment
outcomes over time, which could ultimately enable the adoption of more individualized …
outcomes over time, which could ultimately enable the adoption of more individualized …
Causal Deep Learning: Encouraging Impact on Real-world Problems Through Causality
Causality has the potential to truly transform the way we solve a large number of real-world
problems. Yet, so far, its potential largely remains to be unlocked as causality often requires …
problems. Yet, so far, its potential largely remains to be unlocked as causality often requires …
Estimating average causal effects from patient trajectories
In medical practice, treatments are selected based on the expected causal effects on patient
outcomes. Here, the gold standard for estimating causal effects are randomized controlled …
outcomes. Here, the gold standard for estimating causal effects are randomized controlled …
Allsim: Simulating and benchmarking resource allocation policies in multi-user systems
Numerous real-world systems, ranging from healthcare to energy grids, involve users
competing for finite and potentially scarce resources. Designing policies for resource …
competing for finite and potentially scarce resources. Designing policies for resource …
Counterfactual generative models for time-varying treatments
Estimating the counterfactual outcome of treatment is essential for decision-making in public
health and clinical science, among others. Often, treatments are administered in a …
health and clinical science, among others. Often, treatments are administered in a …
Temporal Uplift Modeling for Online Marketing
In recent years, uplift modeling, also known as individual treatment effect (ITE) estimation,
has seen wide applications in online marketing, such as delivering one-time issuance of …
has seen wide applications in online marketing, such as delivering one-time issuance of …
Navigating causal deep learning
Causal deep learning (CDL) is a new and important research area in the larger field of
machine learning. With CDL, researchers aim to structure and encode causal knowledge in …
machine learning. With CDL, researchers aim to structure and encode causal knowledge in …