Discussion meetings

Discussion meetings are events where articles ('papers for reading') appearing in the Journal of the RSS are presented and discussed. The discussion and authors' replies are then published in the relevant Journal series. 

Read more about our discussion meetings, including guidelines for papers for discussion.

Contact Judith Shorten if you would like to make a written contribution to a discussion meeting or join our mailing list for an early invitation to future meetings.

Next Discussion meeting

Experimental Evaluation of Algorithm-Assisted Human Decision-Making
Tuesday 8 February, 2022, online 4-6pm (GMT)
DeMO: 2-3pm

NB: Please register separately for the Discussion meeting and the DeMO.

Paper: 'Experimental Evaluation of Algorithm-Assisted Human Decision-Making: Application to Pretrial Public Safety Assessment'
Authors: Imai et al.
To be published in JRSSA.

Despite an increasing reliance on fully-automated algorithmic decision-making in our lives, human beings still make consequential decisions. We develop a statistical methodology for experimentally evaluating the causal impacts of algorithmic recommendations on human decisions. We also show how to examine whether algorithmic recommendations improve the fairness of human decisions and derive the optimal decision rules under various settings. We apply the proposed methodology to preliminary data from the first-ever randomized controlled trial that evaluates the pretrial Public Safety Assessment (PSA) in the criminal justice system. A goal of the PSA is to help judges decide which arrested individuals should be released. We find that providing the PSA to the judge has little overall impact on the judge's decisions and subsequent arrestee behavior. However, we find that the PSA may help avoid unnecessarily harsh decisions for female arrestees while it encourages the judge to make stricter decisions for male arrestees who are deemed to be risky. For fairness, the PSA appears to increase the gender bias against males while having little effect on any existing racial differences in judges' decision. Finally, we find that the PSA's recommendations might be unnecessarily severe unless the cost of a new crime is sufficiently high.
Download the preprint

Past Discussion Meetings

Statistical aspects of the Covid-19 pandemic
A multi-paper meeting featuring three discussion papers
Took place at Manchester Central (at the RSS 2021 Conference) and online
8 September 2021, 5.15-7.15pm (BST)

Chair: RSS President Sylvia Richardson

Paper 1:  'Modeling the Covid-19 infection trajectory: a piecewise linear quantile regression'
Authors:  F Jiang (Fudan University, Shanghai, China); Z Zhao, (University of Notre Dame, Indiana, USA); X Shao (University of Illinois Urbana-Champaign, USA)

We propose a piecewise linear quantile trend model to analyse the trajectory of the Covid-19 daily new cases (ie the infection curve) simultaneously across multiple quantiles. The model is intuitive, interpretable and naturally captures the phase transitions of the epidemic growth rate via change-points. Unlike the mean trend model and least squares estimation, our quantile-based approach is robust to outliers, captures heteroscedasticity (commonly exhibited by Covid-19 infection curves) and automatically delivers both point and interval forecasts with minimal assumptions. Building on a self-normalized (SN) test statistic, this paper proposes a novel segmentation algorithm for multiple changepoint estimation. Theoretical guarantees such as segmentation consistency are established under mild and verifiable assumptions. Using the proposed method, we analyse the Covid-19 infection curves in 35 major countries and discover patterns with potentially relevant implications for effectiveness of the pandemic responses by different countries. A simple change-adaptive two-stage forecasting scheme is further designed to generate short-term prediction of Covid-19 cumulative new cases and is shown to deliver accurate forecast valuable to public health decision-making.
Download the preprint
Supplementary material
Paper 2:  'Quantifying the economic response to Covid-19 mitigations and death rates via forecasting Purchasing Managers' Indices using Generalised Network Autoregressive models with exogenous variables' 
Authors: G Nason & J Wei, Imperial College London, UK

Knowledge of the current state of economies, how they respond to Covid-19 mitigations and indicators, and what the future might hold for them is important. We use recently-developed generalised network autoregressive (GNAR) models, using trade determined networks, to model and forecast the Purchasing Managers’ Indices for a number of countries. We use networks that link countries where the links themselves, or their weights, are determined by the degree of export trade between the countries. We extend these models to include node-specific time series exogenous variables (GNARX models), using this to incorporate Covid-19 mitigation stringency indices and Covid-19 death rates into our analysis. The highly parsimonious GNAR models considerably outperform vector autoregressive models in terms of mean-squared forecasting error and our GNARX models themselves outperform GNAR ones. Further mixed frequency modelling predicts the extent to which that the UK economy will be affected by harsher, weaker or no interventions
Download the preprint
Supplementary material
Paper 3: 'Small Data, Big Time - A retrospect of the first weeks of Covid-19'
Author: Q Zhao, Statistical Laboratory, University of Cambridge, UK

This article reviews some early investigations and research studies in the first weeks of the coronavirus disease 2019 (Covid-19) pandemic from a statistician’s perspective. These investigations were based on very small datasets but were momentous in the initial global reactions to the pandemic. The article discusses the initial evidence of high infectiousness of Covid-19 and why that conclusion was not reached faster than in reality. Further reanalyses of some published Covid-19 studies show that the epidemic growth was dramatically underestimated by compartmental models, and the lack of fit could have been clearly identified by simple data visualization. Finally, some lessons for statisticians are discussed.
Download the preprint

View our playlist of recent Discussion Meetings
Read past Discussion Papers

Assumption-lean inference for generalised linear model parameters
Tuesday 6 July 2021

The Discussion Paper was presented by the authors, Stijn Vansteelandt and Oliver Dukes, Ghent University, Belgium, and chaired by Guy Nason.
To be published in Series B; for more information go to the Wiley Online Library.

Download the preprint (PDF). 


Gaussian differential privacy
16 December 2020

The Discussion paper ‘Gaussian Differential Privacy’ by Jinshuo Dong, Aaron Roth, and Weijie J Su was presented by Weijie Su and Jinshuo Dong of the University of Pennsylvania, Philadelphia, USA.

To be published in Series B; for more information go to the Wiley Online Library.

Download the preprint (PDF) - but please note the deadline for contributions has passed.
Watch the meeting (YouTube)
Watch the pre-meeting DeMO (YouTube)