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 Regression Trees and is specifically designed to estimate heterogeneous treatment effects using observational data. The sparsity-inducing component we introduce is motivated by empirical studies where not all the available covariates are relevant, leading to different degrees of sparsity underlying the surfaces of interest in the estimation of individual treatment effects. The extended version presented in this work, which we name Shrinkage Bayesian Causal Forest, is equipped with an additional pair of priors allowing the model to adjust the weight of each covariate through the corresponding number of splits in the tree ensemble. These priors improve the model’s adaptability to sparse data generating processes and allow to perform fully Bayesian feature shrinkage in a framework for treatment effects estimation, and thus to uncover the moderating factors driving heterogeneity. In addition, the method allows prior knowledge about the relevant confounding covariates and the relative magnitude of their impact on the outcome to be incorporated in the model. We illustrate the performance of our method in simulated studies, in comparison to Bayesian Causal Forest and other state-of-the-art models, to demonstrate how it scales up with an increasing number of covariates and how it handles strongly confounded scenarios. Finally, we also provide an example of application using real-world data. Supplementary materials for this article are available online

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 prediction. In this paper, we investigate the problem of estimating heterogeneous treatment effects using non-parametric regression-based methods. Firstly, we introduce the setup and the issues related to conducting causal inference with observational or non-fully randomized data, and how these issues can be tackled with the help of statistical learning tools. Then, we provide a review of state-of-the-art methods, with a particular focus on non-parametric modeling, and we cast them under a unifying taxonomy. After presenting a brief overview on the problem of model selection, we illustrate the performance of some of the methods on three different simulated studies and on a real world example to investigate the effect of participation in school meal programs on health indicators

Ground-state properties of the One-dimensional Kondo Lattice at partial Band-filling

We compute the magnetic structure factor, the singlet correlation function and the momentum distribution of the one-dimensional Kondo lattice model at the density $\rho =0.7$. The density matrix-renormalization group method is used. We show that in the weak-coupling regime, the ground state is paramagnetic. We argue that a Luttinger liquid description of the model in this region is consistent with our calculations . In the strong-coupling regime, the ground state becomes ferromagnetic. The conduction electrons show a spinless-fermion like behavior.Comment: 8 pages, Latex, 5 figures included, to be published in PRB (Rapid Communications

Phase diagram of the one-dimensional Holstein model of spinless fermions

The one-dimensional Holstein model of spinless fermions interacting with dispersionless phonons is studied using a new variant of the density matrix renormalisation group. By examining various low-energy excitations of finite chains, the metal-insulator phase boundary is determined precisely and agrees with the predictions of strong coupling theory in the anti-adiabatic regime and is consistent with renormalisation group arguments in the adiabatic regime. The Luttinger liquid parameters, determined by finite-size scaling, are consistent with a Kosterlitz-Thouless transition.Comment: Minor changes. 4 pages, 4 figures. To appear in Physical Review Letters 80 (1998) 560

Vibration control of the beam of the future linear collider

This paper proposes a new approach for beam stabilization of the future Compact LInear Collider (CLIC). The method attempts to increase the efficiency of traditional methods. It is composed of a hybrid adaptive filtering algorithm that uses both feedback and adaptive control. The scheme uses an estimate of the prediction error to update the adaptive filter's parameters. The strategy of this method is described considering the process environment. The method efficiency is evaluated, and it is demonstrated that it provides high damping, fast vibration suppression, good robustness and easy realization thanks to the simplicity of the computations

Non scientific restrictions to poultry production: main global myth and beliefs.

Projeto/Plano de Ação: 11.11.11.111