Discrete Methods in Statistics: Feature Selection and Fairness-Aware Data Mining

This dissertation is a detailed investigation of issues that arise in models that change discretely. Models are often constructed by either including or excluding features based on some criteria. These discrete changes are challenging to analyze due to correlation between features. Feature selection is the problem of identifying an appropriate set of features to include in a model, while fairness-aware data mining is the problem of needing to remove the \emph{influence} of protected features from a model. This dissertation provides frameworks for understanding each problem and algorithms for accomplishing the desired goal. The feature selection problem is addressed through the framework of sequential hypothesis testing. We elucidate the statistical challenges in repeatedly using inference in this domain and demonstrate how current methods fail to address them. Our algorithms build on classically motivated, multiple testing procedures to control measures of false rejections when using hypothesis testing during forward stepwise regression. Furthermore, these methods have much higher power than recent proposals from the conditional inference literature. The fairness-aware data mining community is grappling with fundamental questions concerning fairness in statistical modeling. Tension exists between identifying explainable differences between groups and discriminatory ones. We provide a framework for understanding the connections between fairness and the use of protected information in modeling. With this discussion in hand, generating fair estimates is straight-forward

Impartial Predictive Modeling: Ensuring Fairness in Arbitrary Models

Fairness aware data mining aims to prevent algorithms from discriminating against protected groups. The literature has come to an impasse as to what constitutes explainable variability as opposed to discrimination. This stems from incomplete discussions of fairness in statistics. We demonstrate that fairness is achieved by ensuring impartiality with respect to sensitive characteristics. As these characteristics are determined outside of the model, the correct description of the statistical task is to ensure impartiality. We provide a framework for impartiality by accounting for different perspectives on the data generating process. This framework yields a set of impartial estimates that are applicable in a wide variety of situations and post-processing tools to correct estimates from arbitrary models. This effectively separates prediction and fairness goals, allowing modelers to focus on generating highly predictive models without incorporating the constraint of fairness

Effects of Pain Neuroscience Education on Physician Assistant Students Understanding of Pain and Attitudes and Beliefs About Pain.

PURPOSE: Evaluate the effectiveness and efficiency of two different pain neuroscience education (PNE) lectures provided to physician assistant (PA) students. Primary outcomes explored were knowledge of pain and shift in attitudes and beliefs about chronic pain after the lecture. METHODS: A PNE lecture was provided at two separate university PA programs. One program received a two-hour PNE lecture with a case-based example. The other program received a one-hour PNE lecture without the casebased example. Measurement of change for pre and post-test pain knowledge and attitudes and beliefs about chronic pain were recorded. RESULTS: Students at both universities showed medium effect size improvements in pain knowledge following the lecture. Only students that received the longer two-hour lecture in the case-based example showed significant improvements with their attitudes and beliefs about patients with chronic pain. CONCLUSION: PA students can increase their knowledge about current pain science through lecture alone, however, case-based learning along with lecture, may be more effective in improving the attitudes and beliefs of PA students regarding patients with chronic pain

Adaptive, Distribution-Free Prediction Intervals for Deep Networks

The machine learning literature contains several constructions for prediction intervals thatare intuitively reasonable but ultimately ad-hoc in that they do not come with provableperformance guarantees. We present methods from the statistics literature that can beused efficiently with neural networks underminimal assumptions with guaranteed performance. We propose a neural network thatoutputs three values instead of a single pointestimate and optimizes a loss function motivated by the standard quantile regression loss. We provide two prediction interval methodswith finite sample coverage guarantees solelyunder the assumption that the observations are independent and identically distributed. The first method leverages the conformal in-ference framework and provides average coverage. The second method provides a new, stronger guarantee by conditioning on the observed data. Lastly, our loss function doesnot compromise the predictive accuracy of thenetwork like other prediction interval methods. We demonstrate the ease of use of our procedures as well as its improvements overother methods on both simulated and realdata. As most deep networks can easily be modified by our method to output predictions with valid prediction intervals, its use should become standard practice, much like reporting standard errors along with mean estimates

Retinoid X receptor activation reverses age-related deficiencies in myelin debris phagocytosis and remyelination.

The efficiency of central nervous system remyelination declines with age. This is in part due to an age-associated decline in the phagocytic removal of myelin debris, which contains inhibitors of oligodendrocyte progenitor cell differentiation. In this study, we show that expression of genes involved in the retinoid X receptor pathway are decreased with ageing in both myelin-phagocytosing human monocytes and mouse macrophages using a combination of in vivo and in vitro approaches. Disruption of retinoid X receptor function in young macrophages, using the antagonist HX531, mimics ageing by reducing myelin debris uptake. Macrophage-specific RXRα (Rxra) knockout mice revealed that loss of function in young mice caused delayed myelin debris uptake and slowed remyelination after experimentally-induced demyelination. Alternatively, retinoid X receptor agonists partially restored myelin debris phagocytosis in aged macrophages. The agonist bexarotene, when used in concentrations achievable in human subjects, caused a reversion of the gene expression profile in multiple sclerosis patient monocytes to a more youthful profile and enhanced myelin debris phagocytosis by patient cells. These results reveal the retinoid X receptor pathway as a positive regulator of myelin debris clearance and a key player in the age-related decline in remyelination that may be targeted by available or newly-developed therapeutics.This work was supported by grants from the UK Multiple Sclerosis Society, Wellcome-Trust, NINDS/NIH Intramural Research Program, Health Research Board Scholars Program, Gates-Cambridge Scholarship, and Spanish Ministry of Economy and Competitiveness (SAF2012- 31483).S