Using machine learning to individualize treatment effect estimation: Challenges and opportunities
A Curth, RW Peck, E McKinney… - Clinical …, 2024 - Wiley Online Library
The use of data from randomized clinical trials to justify treatment decisions for real‐world
patients is the current state of the art. It relies on the assumption that average treatment …
patients is the current state of the art. It relies on the assumption that average treatment …
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 …
Sharp bounds for generalized causal sensitivity analysis
D Frauen, V Melnychuk… - Advances in Neural …, 2024 - proceedings.neurips.cc
Causal inference from observational data is crucial for many disciplines such as medicine
and economics. However, sharp bounds for causal effects under relaxations of the …
and economics. However, sharp bounds for causal effects under relaxations of the …
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 …
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 …
Mining of Switching Sparse Networks for Missing Value Imputation in Multivariate Time Series
Multivariate time series data suffer from the problem of missing values, which hinders the
application of many analytical methods. To achieve the accurate imputation of these missing …
application of many analytical methods. To achieve the accurate imputation of these missing …
Quantitative evaluation of imputation methods using bounds estimation of the coefficient of determination for data-driven models with an application to drilling logs
With the constantly increasing quantity of data recorded in the oil and gas industry, data
analytics and data-driven algorithms are gaining popularity. Meanwhile, they are highly …
analytics and data-driven algorithms are gaining popularity. Meanwhile, they are highly …
CDRM: Causal disentangled representation learning for missing data
M Chen, H Wang, R Wang, Y Peng, H Zhang - Knowledge-Based Systems, 2024 - Elsevier
Missing data pose significant challenges during representation learning of observational
data. The incompleteness of data can result in a deterioration of generative performance in …
data. The incompleteness of data can result in a deterioration of generative performance in …
[PDF][PDF] Dynamic inter-treatment information sharing for heterogeneous treatment effects estimation
Existing heterogeneous treatment effects learners, also known as conditional average
treatment effects (CATE) learners, lack a general mechanism for end-to-end inter-treatment …
treatment effects (CATE) learners, lack a general mechanism for end-to-end inter-treatment …