Search Results for author: Gianluca Baio

Found 7 papers, 4 papers with code

Counterfactual Learning with Multioutput Deep Kernels

1 code implementation20 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.

counterfactual Counterfactual Inference +2

Interpretable Deep Causal Learning for Moderation Effects

no code implementations21 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.

counterfactual

Shrinkage Bayesian Causal Forests for Heterogeneous Treatment Effects Estimation

1 code implementation12 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.

regression Variable Selection

Effect modification in anchored indirect treatment comparisons: Comments on "Matching-adjusted indirect comparisons: Application to time-to-event data"

no code implementations9 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

Estimating Individual Treatment Effects using Non-Parametric Regression Models: a Review

1 code implementation14 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.

Causal Inference Model Selection +1

Modeling outcomes of soccer matches

no code implementations4 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.

A dynamic Bayesian Markov model for health economic evaluations of interventions in infectious disease

1 code implementation21 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

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