Methods and tools for causal discovery and causal inference
Causality is a complex concept, which roots its developments across several fields, such as
statistics, economics, epidemiology, computer science, and philosophy. In recent years, the …
statistics, economics, epidemiology, computer science, and philosophy. In recent years, the …
[HTML][HTML] Moving beyond descriptive studies: harnessing metabolomics to elucidate the molecular mechanisms underpinning host-microbiome phenotypes
Advances in technology and software have radically expanded the scope of metabolomics
studies and allow us to monitor a broad transect of central carbon metabolism in routine …
studies and allow us to monitor a broad transect of central carbon metabolism in routine …
Causal effect inference for structured treatments
We address the estimation of conditional average treatment effects (CATEs) for structured
treatments (eg, graphs, images, texts). Given a weak condition on the effect, we propose the …
treatments (eg, graphs, images, texts). Given a weak condition on the effect, we propose the …
-Intact-VAE: Identifying and Estimating Causal Effects under Limited Overlap
P Wu, K Fukumizu - arXiv preprint arXiv:2110.05225, 2021 - arxiv.org
As an important problem in causal inference, we discuss the identification and estimation of
treatment effects (TEs) under limited overlap; that is, when subjects with certain features …
treatment effects (TEs) under limited overlap; that is, when subjects with certain features …
Comprehensive Causal Machine Learning
M Lechner, J Mareckova - arXiv preprint arXiv:2405.10198, 2024 - arxiv.org
Uncovering causal effects at various levels of granularity provides substantial value to
decision makers. Comprehensive machine learning approaches to causal effect estimation …
decision makers. Comprehensive machine learning approaches to causal effect estimation …
[HTML][HTML] Methods for integrating trials and non-experimental data to examine treatment effect heterogeneity
Estimating treatment effects conditional on observed covariates can improve the ability to
tailor treatments to particular individuals. Doing so effectively requires dealing with potential …
tailor treatments to particular individuals. Doing so effectively requires dealing with potential …
Shrinkage Bayesian causal forests for heterogeneous treatment effects estimation
This article develops a sparsity-inducing version of Bayesian Causal Forests, a recently
proposed nonparametric causal regression model that employs Bayesian Additive …
proposed nonparametric causal regression model that employs Bayesian Additive …
Treatment heterogeneity with survival outcomes
This chapter is accompanied by survlearners, a package that provides well-documented
implementations of the conditional average treatment effects (CATE) estimation strategies …
implementations of the conditional average treatment effects (CATE) estimation strategies …
Understanding causal effects of ride-sourcing subsidy: A novel generative adversarial networks approach
Ride-sourcing platforms offer subsidies for drivers to ensure stable supply capacity for on-
demand ride services. These subsidies guarantee minimum surges in advance to help …
demand ride services. These subsidies guarantee minimum surges in advance to help …
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 …