Estimating individual treatment effects using non-parametric regression models: A review
Large observational data are increasingly available in disciplines such as health, economic
and social sciences, where researchers are interested in causal questions rather than …
and social sciences, where researchers are interested in causal questions rather than …
Bayesian causal forests for multivariate outcomes: application to Irish data from an international large scale education assessment
Abstract Bayesian Causal Forests (BCF) is a causal inference machine learning model
based on the flexible non-parametric regression and classification tool, Bayesian Additive …
based on the flexible non-parametric regression and classification tool, Bayesian Additive …
Multi-modal trajectory forecasting with Multi-scale Interactions and Multi-pseudo-target Supervision
Trajectory forecasting is crucial for the advancement of autonomous vehicles. While much
progress has been made, extant approaches often fall short in accounting for intricate social …
progress has been made, extant approaches often fall short in accounting for intricate social …
Heterogeneity within the Oregon Health Insurance Experiment: An application of causal forests
Existing evidence regarding the effects of Medicaid expansion, largely focused on
aggregate effects, suggests health insurance impacts some health, healthcare utilization …
aggregate effects, suggests health insurance impacts some health, healthcare utilization …
Phenotype-based targeted treatment of SGLT2 inhibitors and GLP-1 receptor agonists in type 2 diabetes
Aims/hypothesis A precision medicine approach in type 2 diabetes could enhance targeting
specific glucose-lowering therapies to individual patients most likely to benefit. We aimed to …
specific glucose-lowering therapies to individual patients most likely to benefit. We aimed to …
Comparison of meta-learners for estimating multi-valued treatment heterogeneous effects
N Acharki, R Lugo, A Bertoncello… - … on Machine Learning, 2023 - proceedings.mlr.press
Abstract Conditional Average Treatment Effects (CATE) estimation is one of the main
challenges in causal inference with observational data. In addition to Machine Learning …
challenges in causal inference with observational data. In addition to Machine Learning …
Phenotype-based targeted treatment of SGLT2 inhibitors and GLP-1 receptor agonists in type 2 diabetes
P Cardoso, KG Young, ATN Nair, R Hopkins… - medRxiv, 2023 - medrxiv.org
A precision medicine approach in type 2 diabetes (T2D) could enhance targeting specific
glucose-lowering therapies to individual patients most likely to benefit. We utilised Bayesian …
glucose-lowering therapies to individual patients most likely to benefit. We utilised Bayesian …
Counterfactual Learning with Multioutput Deep Kernels
In this paper, we address the challenge of performing counterfactual inference with
observational data via Bayesian nonparametric regression adjustment, with a focus on high …
observational data via Bayesian nonparametric regression adjustment, with a focus on high …
Interpretable deep causal learning for moderation effects
In this extended abstract paper, we address the problem of interpretability and targeted
regularization in causal machine learning models. In particular, we focus on the problem of …
regularization in causal machine learning models. In particular, we focus on the problem of …
Estimating heterogeneous treatment effect from survival outcomes via (orthogonal) censoring unbiased learning
Methods for estimating heterogeneous treatment effects (HTE) from observational data have
largely focused on continuous or binary outcomes, with less attention paid to survival …
largely focused on continuous or binary outcomes, with less attention paid to survival …