A Primer on Causality in Data Science

Many questions in Data Science are fundamentally causal in that our objective is to learn the effect of some exposure, randomized or not, on an outcome interest. Even studies that are seemingly non-causal, such as those with the goal of prediction or prevalence estimation, have causal elements, including differential censoring or measurement. As a result, we, as Data Scientists, need to consider the underlying causal mechanisms that gave rise to the data, rather than simply the pattern or association observed in those data. In this work, we review the 'Causal Roadmap' of Petersen and van der Laan (2014) to provide an introduction to some key concepts in causal inference. Similar to other causal frameworks, the steps of the Roadmap include clearly stating the scientific question, defining of the causal model, translating the scientific question into a causal parameter, assessing the assumptions needed to express the causal parameter as a statistical estimand, implementation of statistical estimators including parametric and semi-parametric methods, and interpretation of our findings. We believe that using such a framework in Data Science will help to ensure that our statistical analyses are guided by the scientific question driving our research, while avoiding over-interpreting our results. We focus on the effect of an exposure occurring at a single time point and highlight the use of targeted maximum likelihood estimation (TMLE) with Super Learner.Comment: 26 pages (with references); 4 figure

Temporal trends in annual water yields from the Mackenzie, Saskatchewan-Nelson, Churchill, and Missouri-Mississippi River watersheds in western and northern Canada

Historical temporal trends in annual water yields were examined at 109 hydrometric monitoring stations in the Mackenzie, Saskatchewan-Nelson, Churchill, and Missouri-Mississippi River watersheds from the western Canadian provinces of Alberta, Saskatchewan, and northeastern British Columbia, as well as the Northwest Territories and the eastern portion of the Yukon territory. Effective drainage areas range in size from 325 to 1,680,000 (mean=65,600; median=9,300) km^2^, with associated hydrometric record lengths ranging between 18 and 97 (mean=41; median=38) years. Approximately three-quarters of the stations have no significant trend in average annual flow, with about equal numbers of stations exhibiting significant temporal increases or decreases in annual water yields. Southwestern Alberta and the southwestern Northwest Territories contain small clusters of stations with increasing water yield trends; clusters of decreasing water yield trends are primarily located in central and southern Alberta. The co-location of regions with clusters of both increasing and decreasing trends, or increasing/decreasing and no trends, complicates generalizing broader scale trends in annual water yields across these regions of Canada. No bias in the trend directions appears evident with either watershed size or the length of the hydrometric record

Non-linear spin-wave excitation at low bias fields

Non-linear magnetization dynamics is essential for the operation of many spintronics devices. For microwave assisted switching of magnetic elements the low field regime is of particular interest. In addition a large number of experiments uses high amplitude FMR in order to generate d.c. currents via spin pumping mechanism. Here we use time resolved X-ray magnetic circular dichroism experiments to determine the number density of excited magnons in magnetically soft Ni_80Fe_20 thin films at small bias fields and large rf-excitation amplitudes. Our data shows that the common model of non-linear ferromagnetic resonance is not suitable to describe the low bias field limit. Here we derive a new model of parametric spin-wave excitation which correctly predicts threshold amplitudes and decay rates also at low bias fields. In fact a new series of critical modes with amplitude phase oscillations is found, generalizing the theory of parametric spin-wave excitation

The Contagion Effects of Repeated Activation in Social Networks

Demonstrations, protests, riots, and shifts in public opinion respond to the coordinating potential of communication networks. Digital technologies have turned interpersonal networks into massive, pervasive structures that constantly pulsate with information. Here, we propose a model that aims to analyze the contagion dynamics that emerge in networks when repeated activation is allowed, that is, when actors can engage recurrently in a collective effort. We analyze how the structure of communication networks impacts on the ability to coordinate actors, and we identify the conditions under which large-scale coordination is more likely to emerge.Comment: Submitted for publicatio

Job Search Assistance Programs in Europe: Evaluation Methods and Recent Empirical Findings

Job search assistance programs are part of active labor market policy in many countries. The main characteristics of these activities are an intensi ed counseling and a job search monitoring; in addition, several countries integrate courses teaching further skills into the programs. Job search assistance programs should help to increase the employment chances and to reduce the unemployment duration of the job seekers. In this paper, recent empirical ndings from evaluation studies for 9 European countries are reviewed and implications with regard to the e ectiveness of the activities are derived. To make the ndings of various studies evaluating the di erent programs comparable, the methodological issues of the empirical approaches applied to estimate the causal e ects of the programs are discussed in detail. In addition, relevant characteristics of the unemployment insurance systems, the assignment process, and the content of programs are presented to derive meaningful implications. The comparison of the programs takes account of individual e ects and, if available, cost bene t considerations. The results show that job search assistance programs tend to provide an e ective means to reduce individual unemployment, particularly if provided as combinations of intensive counseling and short-term training coursesJob search assistance programs, active labor market policy, evaluation methods, Europe

Simultaneous inference for misaligned multivariate functional data

We consider inference for misaligned multivariate functional data that represents the same underlying curve, but where the functional samples have systematic differences in shape. In this paper we introduce a new class of generally applicable models where warping effects are modeled through nonlinear transformation of latent Gaussian variables and systematic shape differences are modeled by Gaussian processes. To model cross-covariance between sample coordinates we introduce a class of low-dimensional cross-covariance structures suitable for modeling multivariate functional data. We present a method for doing maximum-likelihood estimation in the models and apply the method to three data sets. The first data set is from a motion tracking system where the spatial positions of a large number of body-markers are tracked in three-dimensions over time. The second data set consists of height and weight measurements for Danish boys. The third data set consists of three-dimensional spatial hand paths from a controlled obstacle-avoidance experiment. We use the developed method to estimate the cross-covariance structure, and use a classification setup to demonstrate that the method outperforms state-of-the-art methods for handling misaligned curve data.Comment: 44 pages in total including tables and figures. Additional 9 pages of supplementary material and reference