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

Hierarchical Bayesian variable selection in the probit model with mixture of nominal and ordinal responses

Multi-class classification problems have been studied for pure nominal and pure ordinal responses. However, there are some cases where the multi-class responses are a mixture of nominal and ordinal. To address this problem we build a hierarchical multinomial probit model with a mixture of both types of responses using latent variables. The nominal responses are each associated to distinct latent variables whereas the ordinal responses have a single latent variable. Our approach first treats the ordinal responses as a single nominal category and then separates the ordinal responses within this category. We introduce sparsity into the model using Bayesian variable selection (BVS) within the regression in order to improve variable selection classification accuracy. Two indicator vectors (indicating presence of the covariate) are used, one for nominal and one for ordinal responses. We develop efficient posteriorsampling. Using simulated data, we compare the classification accuracy of our method to existing ones

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

Efficient Monte Carlo Estimation of the Expected Value of Sample Information Using Moment Matching

BACKGROUND: The Expected Value of Sample Information (EVSI) is used to calculate the economic value of a new research strategy. Although this value would be important to both researchers and funders, there are very few practical applications of the EVSI. This is due to computational difficulties associated with calculating the EVSI in practical health economic models using nested simulations. METHODS: We present an approximation method for the EVSI that is framed in a Bayesian setting and is based on estimating the distribution of the posterior mean of the incremental net benefit across all possible future samples, known as the distribution of the preposterior mean. Specifically, this distribution is estimated using moment matching coupled with simulations that are available for probabilistic sensitivity analysis, which is typically mandatory in health economic evaluations. RESULTS: This novel approximation method is applied to a health economic model that has previously been used to assess the performance of other EVSI estimators and accurately estimates the EVSI. The computational time for this method is competitive with other methods. CONCLUSION: We have developed a new calculation method for the EVSI which is computationally efficient and accurate. LIMITATIONS: This novel method relies on some additional simulation so can be expensive in models with a large computational cost

Climate drives community-wide divergence within species over a limited spatial scale: evidence from an oceanic island

Geographic isolation substantially contributes to species endemism on oceanic islands when speciation involves the colonisation of a new island. However, less is understood about the drivers of speciation within islands. What is lacking is a general understanding of the geographic scale of gene flow limitation within islands, and thus the spatial scale and drivers of geographical speciation within insular contexts. Using a community of beetle species, we show that when dispersal ability and climate tolerance are restricted, microclimatic variation over distances of only a few kilometres can maintain strong geographic isolation extending back several millions of years. Further to this, we demonstrate congruent diversification with gene flow across species, mediated by Quaternary climate oscillations that have facilitated a dynamic of isolation and secondary contact. The unprecedented scale of parallel species responses to a common environmental driver for evolutionary change has profound consequences for understanding past and future species responses to climate variation

About the first experiment at JINR nuclotron deuteron beam with energy 2.52 gev on investigation of transmutation of I-129, NP-237, PU-238 and PU-239 in the field of neutrons generated in pbtarget with U-blanket

The experiment described in this communication is a part of the scientific program „Investigations of physical aspects of electronuclear method of energy production and transmutation of radioactive waste of atomic energetic using relativistic beams from the JINR Synchrophasotron/Nuclotron“ - the project „Energy plus Transmutation“. The performing of the first experiment at deuteron beam with energy 2.52 GeV at the electronuclear setup which consists of Pb-target with U-blanket (206.4 kg of natural uranium) and transmutation samples and its preliminary results are described. The hermetic samples of isotopes of I-129, Np-237, Pu-238 and Pu-239 which are produced in atomic reactors and industry setups which use nuclear materials and nuclear technologies were irradiated in the field of electronuclear neutrons produced in the Pbtarget surrounded with the U-blanket setup “Energy plus transmutation”. The estimations of its transmutations (radioecological aspect) were obtained in result of measurements of gamma activities of these samples. The information about space-energy distribution of neutrons in the volume of the Pb-target and the U-blanket was obtained with help of sets of activation threshold detectors (Al, V, Cu, Co, Y, In, I, Ta, Au, W, Bi and other), solid state nuclear track detectors, He-3 neutron detectors and nuclear emulsions

The XMM Cluster Survey: Exploring scaling relations and completeness of the Dark Energy Survey Year 3 redMaPPer cluster catalogue

We cross-match and compare characteristics of galaxy clusters identified in observations from two sky surveys using two completely different techniques. One sample is optically selected from the analysis of three years of Dark Energy Survey observations using the redMaPPer cluster detection algorithm. The second is X-ray selected from XMM observations analysed by the XMM Cluster Survey. The samples comprise a total area of 57.4 deg$^2$, bounded by the area of 4 contiguous XMM survey regions that overlap the DES footprint. We find that the X-ray selected sample is fully matched with entries in the redMaPPer catalogue, above $\lambda>$20 and within 0.1$< z <$0.9. Conversely, only 38\% of the redMaPPer catalogue is matched to an X-ray extended source. Next, using 120 optically clusters and 184 X-ray selected clusters, we investigate the form of the X-ray luminosity-temperature ($L_{X}-T_{X}$), luminosity-richness ($L_{X}-\lambda$) and temperature-richness ($T_{X}-\lambda$) scaling relations. We find that the fitted forms of the $L_{X}-T_{X}$ relations are consistent between the two selection methods and also with other studies in the literature. However, we find tentative evidence for a steepening of the slope of the relation for low richness systems in the X-ray selected sample. When considering the scaling of richness with X-ray properties, we again find consistency in the relations (i.e., $L_{X}-\lambda$ and $T_{X}-\lambda$) between the optical and X-ray selected samples. This is contrary to previous similar works that find a significant increase in the scatter of the luminosity scaling relation for X-ray selected samples compared to optically selected samples.Comment: Accepted for publication to MNRA