Expected Future Earnings, Taxation, and University Enrollment: A Microeconometric Model with Uncertainty

Taxation changes the expectations of prospective university students about their future level and uncertainty of after-tax income. To estimate the impact of taxes on university enrollment, we develop and estimate a structural microeconometric model, in which a high-school graduate decides to enter university studies if expected lifetime utility from this choice is greater than that anticipated from starting to work right away. We estimate the ex-ante future paths of the expectation and variance of net income for German high-school graduates, using only information available to those graduates at the time of the enrollment decision, accounting for multiple nonrandom selection and employing a microsimulation model to account for taxation. In addition to income uncertainty, the enrollment model takes into account university dropout and unemployment risks, as well as potential credit constraints. The estimation results are consistent with expectations. First, higher risk-adjusted returns to an academic education increase the probability of university enrollment. Second, high-school graduates are moderately risk averse, as indicated by the Arrow-Pratt coefficient of risk aversion estimated within the model. Thus, higher uncertainty among academics decreases enrollment rates. A simulation based on the estimated structural model indicates that a revenue-neutral, flat-rate tax reform with an unchanged basic tax allowance would increase enrollment rates for men in Germany because of the higher expected net income in the higher income range.University Enrollment, Income Taxation, Flat Tax, Income Risk, Risk Aversion

Deformable Registration through Learning of Context-Specific Metric Aggregation

We propose a novel weakly supervised discriminative algorithm for learning context specific registration metrics as a linear combination of conventional similarity measures. Conventional metrics have been extensively used over the past two decades and therefore both their strengths and limitations are known. The challenge is to find the optimal relative weighting (or parameters) of different metrics forming the similarity measure of the registration algorithm. Hand-tuning these parameters would result in sub optimal solutions and quickly become infeasible as the number of metrics increases. Furthermore, such hand-crafted combination can only happen at global scale (entire volume) and therefore will not be able to account for the different tissue properties. We propose a learning algorithm for estimating these parameters locally, conditioned to the data semantic classes. The objective function of our formulation is a special case of non-convex function, difference of convex function, which we optimize using the concave convex procedure. As a proof of concept, we show the impact of our approach on three challenging datasets for different anatomical structures and modalities.Comment: Accepted for publication in the 8th International Workshop on Machine Learning in Medical Imaging (MLMI 2017), in conjunction with MICCAI 201

Stated and revealed heterogeneous risk preferences in educational choice

Stated survey measures of risk preferences are increasingly being used in the literature, and they have been compared to revealed risk aversion primarily by means of experiments such as lottery choice tasks. In this paper, we investigate educational choice, which involves the comparison of risky future income paths and therefore depends on risk and time preferences. In contrast to experimental settings, educational choice is one of the most important economic decisions taken by individuals, and we observe actual choices in representative panel data. We estimate a structural microeconometric model to jointly reveal risk and time preferences based on educational choices, allowing for unobserved heterogeneity in the Arrow-Pratt risk aversion parameter. The probabilities of membership in the latent classes of persons with higher or lower risk aversion are modelled as functions of stated risk preferences elicited in the survey using standard questions. Two types are identified: A small group with high risk aversion and a large group with low risk aversion. The results indicate that persons who state that they are generally less willing to take risks in the survey tend to belong to the latent class with higher revealed risk aversion, which indicates consistency of stated and revealed risk preferences. The relevance of the distinction between the two types for educational choice is demonstrated by their distinct reactions to a simulated tax policy scenario

Expected future earnings, taxation, and university enrollment: a microeconometric model with uncertainty

Taxation changes the expectations of prospective university students about their future level and uncertainty of after-tax income. To estimate the impact of taxes on university enrollment, we develop and estimate a structural microeconometric model, in which a high-school graduate decides to enter university studies if expected lifetime utility from this choice is greater than that anticipated from starting to work right away. We estimate the ex-ante future paths of the expectation and variance of net income for German high-school graduates, using only information available to those graduates at the time of the enrollment decision, accounting for multiple nonrandom selection and employing a microsimulation model to account for taxation. In addition to income uncertainty, the enrollment model takes into account university dropout and unemployment risks, as well as potential credit constraints. The estimation results are consistent with expectations. First, higher risk-adjusted returns to an academic education increase the probability of university enrollment. Second, high-school graduates are moderately risk averse, as indicated by the Arrow-Pratt coefficient of risk aversion estimated within the model. Thus, higher uncertainty among academics decreases enrollment rates. A simulation based on the estimated structural model indicates that a revenue-neutral, flat-rate tax reform with an unchanged basic tax allowance would increase enrollment rates for men in Germany because of the higher expected net income in the higher income range

Robustness Stress Testing in Medical Image Classification

Deep neural networks have shown impressive performance for image-based disease detection. Performance is commonly evaluated through clinical validation on independent test sets to demonstrate clinically acceptable accuracy. Reporting good performance metrics on test sets, however, is not always a sufficient indication of the generalizability and robustness of an algorithm. In particular, when the test data is drawn from the same distribution as the training data, the iid test set performance can be an unreliable estimate of the accuracy on new data. In this paper, we employ stress testing to assess model robustness and subgroup performance disparities in disease detection models. We design progressive stress testing using five different bidirectional and unidirectional image perturbations with six different severity levels. As a use case, we apply stress tests to measure the robustness of disease detection models for chest X-ray and skin lesion images, and demonstrate the importance of studying class and domain-specific model behaviour. Our experiments indicate that some models may yield more robust and equitable performance than others. We also find that pretraining characteristics play an important role in downstream robustness. We conclude that progressive stress testing is a viable and important tool and should become standard practice in the clinical validation of image-based disease detection models

Large-scale Quality Control of Cardiac Imaging in Population Studies: Application to UK Biobank

In large population studies such as the UK Biobank (UKBB), quality control of the acquired images by visual assessment is unfeasible. In this paper, we apply a recently developed fully-automated quality control pipeline for cardiac MR (CMR) images to the first 19,265 short-axis (SA) cine stacks from the UKBB. We present the results for the three estimated quality metrics (heart coverage, inter-slice motion and image contrast in the cardiac region) as well as their potential associations with factors including acquisition details and subject-related phenotypes. Up to 14.2% of the analysed SA stacks had sub-optimal coverage (i.e. missing basal and/or apical slices), however most of them were limited to the first year of acquisition. Up to 16% of the stacks were affected by noticeable inter-slice motion (i.e. average inter-slice misalignment greater than 3.4 mm). Inter-slice motion was positively correlated with weight and body surface area. Only 2.1% of the stacks had an average end-diastolic cardiac image contrast below 30% of the dynamic range. These findings will be highly valuable for both the scientists involved in UKBB CMR acquisition and for the ones who use the dataset for research purposes

Algorithmic encoding of protected characteristics in chest X-ray disease detection models

Background It has been rightfully emphasized that the use of AI for clinical decision making could amplify health disparities. An algorithm may encode protected characteristics, and then use this information for making predictions due to undesirable correlations in the (historical) training data. It remains unclear how we can establish whether such information is actually used. Besides the scarcity of data from underserved populations, very little is known about how dataset biases manifest in predictive models and how this may result in disparate performance. This article aims to shed some light on these issues by exploring methodology for subgroup analysis in image-based disease detection models. Methods We utilize two publicly available chest X-ray datasets, CheXpert and MIMIC-CXR, to study performance disparities across race and biological sex in deep learning models. We explore test set resampling, transfer learning, multitask learning, and model inspection to assess the relationship between the encoding of protected characteristics and disease detection performance across subgroups. Findings We confirm subgroup disparities in terms of shifted true and false positive rates which are partially removed after correcting for population and prevalence shifts in the test sets. We find that transfer learning alone is insufficient for establishing whether specific patient information is used for making predictions. The proposed combination of test-set resampling, multitask learning, and model inspection reveals valuable insights about the way protected characteristics are encoded in the feature representations of deep neural networks. Interpretation Subgroup analysis is key for identifying performance disparities of AI models, but statistical differences across subgroups need to be taken into account when analyzing potential biases in disease detection. The proposed methodology provides a comprehensive framework for subgroup analysis enabling further research into the underlying causes of disparities. Funding European Research Council Horizon 2020, UK Research and Innovation

Stratified decision forests for accurate anatomical landmark localization in cardiac images

Accurate localization of anatomical landmarks is an important step in medical imaging, as it provides useful prior information for subsequent image analysis and acquisition methods. It is particularly useful for initialization of automatic image analysis tools (e.g. segmentation and registration) and detection of scan planes for automated image acquisition. Landmark localization has been commonly performed using learning based approaches, such as classifier and/or regressor models. However, trained models may not generalize well in heterogeneous datasets when the images contain large differences due to size, pose and shape variations of organs. To learn more data-adaptive and patient specific models, we propose a novel stratification based training model, and demonstrate its use in a decision forest. The proposed approach does not require any additional training information compared to the standard model training procedure and can be easily integrated into any decision tree framework. The proposed method is evaluated on 1080 3D highresolution and 90 multi-stack 2D cardiac cine MR images. The experiments show that the proposed method achieves state-of-theart landmark localization accuracy and outperforms standard regression and classification based approaches. Additionally, the proposed method is used in a multi-atlas segmentation to create a fully automatic segmentation pipeline, and the results show that it achieves state-of-the-art segmentation accuracy

High fidelity image counterfactuals with probabilistic causal models

We present a general causal generative modelling framework for accurate estimation of high fidelity image counterfactuals with deep structural causal models. Estimation of interventional and counterfactual queries for high-dimensional structured variables, such as images, remains a challenging task. We leverage ideas from causal mediation analysis and advances in generative modelling to design new deep causal mechanisms for structured variables in causal models. Our experiments demonstrate that our proposed mechanisms are capable of accurate abduction and estimation of direct, indirect and total effects as measured by axiomatic soundness of counterfactuals