Social scientists often estimate models from correlational data, where the independent variable has not been exogenously manipulated; they also make implicit or explicit causal claims based on these models. When can these claims be made? We answer this question by first discussing design and estimation conditions under which model estimates can be interpreted, using the randomized experiment as the gold standard. We show how endogeneity – which includes omitted variables, omitted selection, simultaneity, common-method variance, and measurement error – renders estimates causally uninterpretable. Second, we present methods that allow researchers to test causal claims in situations where randomization is not possible or when causal interpretation could be confounded; these methods include fixed-effects panel, sample selection, instrumental variable, regression discontinuity, and difference-in-differences models. Third, we take stock of the methodological rigor with which causal claims are being made in a social sciences discipline by reviewing a representative sample of 110 articles on leadership published in the previous 10 years in top-tier journals. Our key finding is that researchers fail to address at least 66% and up to 90% of design and estimation conditions that make causal claims invalid. We conclude by offering 10 suggestions on how to improve non-experimental research.

On making causal claims : A review and recommendations

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An Introduction To The Theory Of Point Processes

Noel Cressie and Christopher WikleHardcover: 624 pagesYear: 2011Publisher: John WileyISBN-13: 978-0471692744Here is the new reference book about spatial and spatio-temporal statistical modeling! No...

Statistics for Spatio-Temporal Data

An introduction to the theory of point processes

In this work, we examine a novel forecasting approach for COVID-19 case prediction that uses Graph Neural Networks and mobility data. In contrast to existing time series forecasting models, the proposed approach learns from a single large-scale spatio-temporal graph, where nodes represent the region-level human mobility, spatial edges represent the human mobility based inter-region connectivity, and temporal edges represent node features through time. We evaluate this approach on the US county level COVID-19 dataset, and demonstrate that the rich spatial and temporal information leveraged by the graph neural network allows the model to learn complex dynamics. We show a 6% reduction of RMSLE and an absolute Pearson Correlation improvement from 0.9978 to 0.998 compared to the best performing baseline models. This novel source of information combined with graph based deep learning approaches can be a powerful tool to understand the spread and evolution of COVID-19. We encourage others to further develop a novel modeling paradigm for infectious disease based on GNNs and high resolution mobility data.

Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks

Self-exciting spatio-temporal point process models predict the rate of events as a function of space, time, and the previous history of events. These models naturally capture triggering and clustering behavior, and have been widely used in fields where spatio-temporal clustering of events is observed, such as earthquake modeling, infectious disease, and crime. In the past several decades, advances have been made in estimation, inference, simulation, and diagnostic tools for self-exciting point process models. In this review, I describe the basic theory, survey related estimation and inference techniques from each field, highlight several key applications, and suggest directions for future research.

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A Review of Self-Exciting Spatio-Temporal Point Processes and Their Applications

/pdf/a-review-of-self-exciting-spatio-temporal-point-processes-1u98rd8h0d.pdf

Scientific progress depends on good research, and good research needs good statistics. But statistical analysis is tricky to get right, even for the best and brightest of us. You'd be surprised how many scientists are doing it wrong. Statistics Done Wrong is a pithy, essential guide to statistical blunders in modern science that will show you how to keep your research blunder-free. You'll examine embarrassing errors and omissions in recent research, learn about the misconceptions and scientific politics that allow these mistakes to happen, and begin your quest to reform the way you and your peers do statistics. You'll find advice on: Asking the right question, designing the right experiment, choosing the right statistical analysis, and sticking to the plan How to think about p values, significance, insignificance, confidence intervals, and regression Choosing the right sample size and avoiding false positives Reporting your analysis and publishing your data and source code Procedures to follow, precautions to take, and analytical software that can help Scientists: Read this concise, powerful guide to help you produce statistically sound research. Statisticians: Give this book to everyone you know. The first step toward statistics done right is Statistics Done Wrong.

Statistics Done Wrong: The Woefully Complete Guide

Crime has both varying patterns in space, related to features of the environment, economy and policing, and patterns in time arising from criminal behaviour, such as retaliation. Serious crimes may also be presaged by minor crimes of disorder. We demonstrate that these spatial and temporal patterns are generally confounded, requiring analyses to take both into account, and propose a spatiotemporal self‐exciting point process model that incorporates spatial features, near repeat and retaliation effects, and triggering. We develop inference methods and diagnostic tools, such as residual maps, for this model, and through extensive simulation and crime data obtained from Pittsburgh, Pennsylvania, demonstrate its properties and usefulness.

Self‐exciting point processes with spatial covariates: modelling the dynamics of crime

COVID-19 vaccine hesitancy threatens pandemic control efforts. We evaluated vaccine hesitancy in the US by employment status and occupation category during the COVID-19 vaccine rollout. US adults 18-64 years completed an online COVID-19 survey 3,179,174 times from January 6-May 19, 2021. Data was aggregated by month. Survey weights matched the sample to the US population age, gender, and state profile. Weighted percentages and 95% confidence intervals (CI) were calculated. Changes in vaccine hesitancy from January-May varied widely by employment status (e.g., -7.8% [95%CI, -8.2 - -7.5] among those working outside the home, a 26.6% decrease; -13.3% [95%CI, -13.7 - -13.0] among those not working for pay, a 44.9% decrease), and occupation category (e.g., -15.9% [95%CI, -17.7 - -14.2] in production, a 39.3% decrease; -1.4% [95%CI, -3.8 - -1.0] in construction/extraction, a 3.0% decrease). April 20-May 19, 2021, vaccine hesitancy ranged from 7.3% (95%CI, 6.7 - 7.8) in computer/mathematical professions to 45.2% (95%CI, 43.2-46.8) in construction/extraction. Hesitancy was 9.0% (95%CI, 8.6-9.3) among educators and 14.5% (95%CI, 14.0-15.0) among healthcare practitioners/technicians. While the prevalence of reasons for hesitancy differed by occupation, over half of employed hesitant participants reported concern about side effects (51.7%) and not trusting COVID-19 vaccines (51.3%), whereas only 15.0% didn't like vaccines in general. Over a third didn't believe they needed the vaccine, didn't trust the government, and/or were waiting to see if it was safe. In this massive national survey of adults 18-64 years, vaccine hesitancy varied widely by occupation. Reasons for hesitancy indicate messaging about safety and addressing trust are paramount.

Alex Reinhart

Papers

A Review of Self-Exciting Spatio-Temporal Point Processes and Their Applications

A Review of Self-Exciting Spatio-Temporal Point Processes and Their Applications

Statistics Done Wrong: The Woefully Complete Guide

Self‐exciting point processes with spatial covariates: modelling the dynamics of crime

COVID-19 vaccine hesitancy January-May 2021 among 18-64 year old US adults by employment and occupation.