Bayesian Learning in the Counterfactual World

Recent years have witnessed a surging interest towards the use of machine learning tools for causal inference. In contrast to the usual large data settings where the primary goal is prediction, many disciplines, such as health, economic and social sciences, are instead interested in causal questions. Learning individualized responses to an intervention is a crucial task in many applied fields (e.g., precision medicine, targeted advertising, precision agriculture, etc.) where the ultimate goal is to design optimal and highly-personalized policies based on individual features. In this work, I thus tackle the problem of estimating causal effects of an intervention that are heterogeneous across a population of interest and depend on an individual set of characteristics (e.g., a patient's clinical record, user's browsing history, etc..) in high-dimensional observational data settings. This is done by utilizing Bayesian Nonparametric or Probabilistic Machine Learning tools that are specifically adjusted for the causal setting and have desirable uncertainty quantification properties, with a focus on the issues of interpretability/explainability and inclusion of domain experts' prior knowledge. I begin by introducing terminology and concepts from causality and causal reasoning in the first chapter. Then I include a literature review of some of the state-of-the-art regression-based methods for heterogeneous treatment effects estimation, with an attempt to build a unifying taxonomy and lay down the finite-sample empirical properties of these models. The chapters forming the core of the dissertation instead present some novel methods addressing existing issues in individualized causal effects estimation: Chapter 3 develops both a Bayesian tree ensemble method and a deep learning architecture to tackle interpretability, uncertainty coverage and targeted regularization; Chapter 4 instead introduces a novel multi-task Deep Kernel Learning method particularly suited for multi-outcome | multi-action scenarios. The last chapter concludes with a discussion

Efficient XAI Techniques: A Taxonomic Survey

Recently, there has been a growing demand for the deployment of Explainable Artificial Intelligence (XAI) algorithms in real-world applications. However, traditional XAI methods typically suffer from a high computational complexity problem, which discourages the deployment of real-time systems to meet the time-demanding requirements of real-world scenarios. Although many approaches have been proposed to improve the efficiency of XAI methods, a comprehensive understanding of the achievements and challenges is still needed. To this end, in this paper we provide a review of efficient XAI. Specifically, we categorize existing techniques of XAI acceleration into efficient non-amortized and efficient amortized methods. The efficient non-amortized methods focus on data-centric or model-centric acceleration upon each individual instance. In contrast, amortized methods focus on learning a unified distribution of model explanations, following the predictive, generative, or reinforcement frameworks, to rapidly derive multiple model explanations. We also analyze the limitations of an efficient XAI pipeline from the perspectives of the training phase, the deployment phase, and the use scenarios. Finally, we summarize the challenges of deploying XAI acceleration methods to real-world scenarios, overcoming the trade-off between faithfulness and efficiency, and the selection of different acceleration methods.Comment: 15 pages, 3 figure

Localization of adaptive variants in human genomes using averaged one-dependence estimation.

Statistical methods for identifying adaptive mutations from population genetic data face several obstacles: assessing the significance of genomic outliers, integrating correlated measures of selection into one analytic framework, and distinguishing adaptive variants from hitchhiking neutral variants. Here, we introduce SWIF(r), a probabilistic method that detects selective sweeps by learning the distributions of multiple selection statistics under different evolutionary scenarios and calculating the posterior probability of a sweep at each genomic site. SWIF(r) is trained using simulations from a user-specified demographic model and explicitly models the joint distributions of selection statistics, thereby increasing its power to both identify regions undergoing sweeps and localize adaptive mutations. Using array and exome data from 45 ‡Khomani San hunter-gatherers of southern Africa, we identify an enrichment of adaptive signals in genes associated with metabolism and obesity. SWIF(r) provides a transparent probabilistic framework for localizing beneficial mutations that is extensible to a variety of evolutionary scenarios