Essays on Tree-based Methods for Prediction and Causal Inference

The first chapter of this thesis contains an application of causal forests to a residential electricity smart meter trial dataset. Household specific estimates are obtained for the effect of a Time-of-Use pricing scheme on peak demand. The most and least responsive households differ across education, age, employment status, and past electricity consumption. The results suggest that past consumption information is more useful than pre-trial survey information, which includes building characteristics, household characteristics, and responses to appliance usage questions. The second chapter explores new variations of Bayesian tree-based machine learning algorithms. Bayesian Additive Regression Trees (BART) (Chipman et al. 2010) and Bayesian Causal Forests (BCF) (Hahn et al. 2020) are state-of-the-art machine learning methods for prediction and causal inference. A number of existing implementations of BART make use of Markov Chain Monte Carlo algorithms, which can be computationally expensive when applied to high-dimensional datasets, do not always perform well in terms of mixing of chains, and have limited parallelizability. The second chapter introduces four variations of BART that do not rely on MCMC: 1. An improved implementation of the existing method BART-BMA (Hernandez et al. 2018), which averages over sum-of-tree models found by a model search algorithm, performs well on high-dimensional datasets, and produces more interpretable output than other BART implementations because the output includes a comparatively small number of sum-of-tree models. %, each of which contains (under the default settings) 5 trees. Improvements are made to the model search algorithm, calculation of predictions, and credible intervals.% The algorithm is entirely deterministic. 2. A treatment effect estimation algorithm that combines the model structure of BCF with the implementation of BART-BMA (BCF-BMA). This method successfully accounts for confounding on observables using the BCF parameterization, while retaining the parsimonious model selection approach of BART-BMA. 3. A simple alternative BART implementation algorithm that uses importance sampling of models (BART-IS). This approach contrasts with existing MCMC and model-search based approaches in that BART-IS makes fast data-independent draws of many sum-of-tree models. The advantages of this approach are that it is straightforward to implement, fast, and trivially parallelizable. 4. Bayesian Causal Forests using Importance Sampling (BCF-IS). This is a combination of the BCF model framework with the BART-IS implementation. BART-IS and BCF-IS exhibit comparable performance to BART-MCMC and BCF across a large number of simulated datasets. The second chapter also includes some illustrative applications. The methods are extendable to multiple treatments, multivariate outcomes, and panel data methods. The third chapter of this thesis describes how the methods introduced in the second chapter can be generalized from regression and treatment effect estimation for continuous outcomes, to a range of models with various link functions and outcome variables. As examples of how to apply the general approach, Logit-BART-BMA and Logit-BART-IS are introduced with illustrative applications

Type I Tobit Bayesian Additive Regression Trees for Censored Outcome Regression

Censoring occurs when an outcome is unobserved beyond some threshold value. Methods that do not account for censoring produce biased predictions of the unobserved outcome. This paper introduces Type I Tobit Bayesian Additive Regression Tree (TOBART-1) models for censored outcomes. Simulation results and real data applications demonstrate that TOBART-1 produces accurate predictions of censored outcomes. TOBART-1 provides posterior intervals for the conditional expectation and other quantities of interest. The error term distribution can have a large impact on the expectation of the censored outcome. Therefore the error is flexibly modeled as a Dirichlet process mixture of normal distributions.Comment: 51 page

Static and Dynamic BART for Rank-Order Data

Ranking lists are often provided at regular time intervals by one or multiple rankers in a range of applications, including sports, marketing, and politics. Most popular methods for rank-order data postulate a linear specification for the latent scores, which determine the observed ranks, and ignore the temporal dependence of the ranking lists. To address these issues, novel nonparametric static (ROBART) and autoregressive (ARROBART) models are introduced, with latent scores defined as nonlinear Bayesian additive regression tree functions of covariates. To make inferences in the dynamic ARROBART model, closed-form filtering, predictive, and smoothing distributions for the latent time-varying scores are derived. These results are applied in a Gibbs sampler with data augmentation for posterior inference. The proposed methods are shown to outperform existing competitors in simulation studies, and the advantages of the dynamic model are demonstrated by forecasts of weekly pollster rankings of NCAA football teams.Comment: The Supplementary Material is available upon request to the author

Digital transformation of peatland eco-innovations (‘Paludiculture’): Enabling a paradigm shift towards the real-time sustainable production of ‘green-friendly’ products and services

The world is heading in the wrong direction on carbon emissions where we are not on track to limit global warming to 1.5 degrees C; Ireland is among the countries where overall emissions have continued to rise. The development of wettable peatland products and services (termed 'Paludiculture') present significant opportunities for enabling a transition away from peat-harvesting (fossil fuels) to developing 'green' eco-innovations. However, this must be balanced with sustainable carbon sequestration and environmental protection. This complex transition from 'brown to green' must be met in real time by enabling digital technologies across the full value chain. This will potentially necessitate creation of new green-business models with the potential to support disruptive innovation. This timely paper describes digital transformation of paludiculture-based eco-innovation that will potentially lead to a paradigm shift towards using smart digital technologies to address efficiency of products and services along with future-proofing for climate change. Digital transform of paludiculture also aligns with the 'Industry 5.0 -a human-centric solution'. However, companies supporting peatland innovation may lack necessary standards, data-sharing or capabilities that can also affect viable business model propositions that can jeopardize economic, political and social sustainability. Digital solutions may reduce costs, increase productivity, improve produce develop, and achieve faster time to market for paludiculture. Digitisation also enables information systems to be open, interoperable, and user-friendly. This constitutes the first study to describe the digital transformation of paludiculture, both vertically and horizontally, in order to inform sustainability that includes process automation via AI, machine learning, IoT-Cloud informed sensors and robotics, virtual and augmented reality, and blockchain for cyber-physical systems. Thus, the aim of this paper is to describe the applicability of digital transformation to actualize the benefits and opportunities of paludiculture activities and enterprises in the Irish midlands with a global orientation.info:eu-repo/semantics/publishedVersio

Discussion of: "Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects"

Contributed discussion included in P. Richard Hahn. Jared S. Murray. Carlos M. Carvalho. "Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects (with Discussion)." Bayesian Anal. 15 (3) 965 - 1056, September 2020. https://doi.org/10.1214/19-BA119

Redeploying β-lactam antibiotics as a novel antivirulence strategy for the treatment of methicillin-resistant <i>Staphylococcus aureus</i> infections

Innovative approaches to the use of existing antibiotics is an important strategy in efforts to address the escalating antimicrobial resistance crisis. We report a new approach to the treatment of methicillin-resistant Staphylococcus aureus (MRSA) infections by demonstrating that oxacillin can be used to significantly attenuate the virulence of MRSA despite the pathogen being resistant to this drug. Using mechanistic in vitro assays and in vivo models of invasive pneumonia and sepsis, we show that oxacillin-treated MRSA strains are significantly attenuated in virulence. This effect is based primarily on the oxacillin-dependent repression of the accessory gene regulator quorum-sensing system and altered cell wall architecture, which in turn lead to increased susceptibility to host killing of MRSA. Our data indicate that beta-lactam antibiotics should be included in the treatment regimen as an adjunct antivirulence therapy for patients with MRSA infections. This would represent an important change to current clinical practice for treatment of MRSA infection, with the potential to significantly improve patient outcomes in a safe, cost-effective manner