1 code implementation • 20 Nov 2022 • Alberto Caron, Gianluca Baio, Ioanna Manolopoulou
In this paper, we address the challenge of performing counterfactual inference with observational data via Bayesian nonparametric regression adjustment, with a focus on high-dimensional settings featuring multiple actions and multiple correlated outcomes.
no code implementations • 21 Jun 2022 • Alberto Caron, Gianluca Baio, Ioanna Manolopoulou
In this extended abstract paper, we address the problem of interpretability and targeted regularization in causal machine learning models.
1 code implementation • 12 Feb 2021 • Alberto Caron, Gianluca Baio, Ioanna Manolopoulou
This paper develops a sparsity-inducing version of Bayesian Causal Forests, a recently proposed nonparametric causal regression model that employs Bayesian Additive Regression Trees and is specifically designed to estimate heterogeneous treatment effects using observational data.
no code implementations • 9 Dec 2020 • Antonio Remiro-Azócar, Anna Heath, Gianluca Baio
The LASSO is more efficient because it selects a subset of the maximal set of covariates but there are no cross-study imbalances in effect modifiers inducing bias.
Variable Selection Methodology Applications
1 code implementation • 14 Sep 2020 • Alberto Caron, Gianluca Baio, Ioanna Manolopoulou
Large observational data are increasingly available in disciplines such as health, economic and social sciences, where researchers are interested in causal questions rather than prediction.
no code implementations • 4 Jul 2018 • Alkeos Tsokos, Santhosh Narayanan, Ioannis Kosmidis, Gianluca Baio, Mihai Cucuringu, Gavin Whitaker, Franz J. Király
The parameters of the Bradley-Terry extensions are estimated by maximizing the log-likelihood, or an appropriately penalized version of it, while the posterior densities of the parameters of the hierarchical Poisson log-linear model are approximated using integrated nested Laplace approximations.
1 code implementation • 21 Dec 2015 • Katrin Haeussler, Ardo van den Hout, Gianluca Baio
Standard MMs are static, whereas ODE systems are usually dynamic and account for herd immunity which is crucial to prevent overestimation of infection prevalence.
Methodology