"Nature does play dice!" - Randomisation without an experiment

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Emerging Applications Section

Tuesday 09 February 2016, 02:55pm - 05:45pm

Location Royal Statistical Society, 12 Errol Street, London, EC1Y 8LX

This event is being live streamed at rss.org.uk/live

In this session we explore three different data situations where units have been randomised in some way without a trial taking place. These circumstances are referred to a natural or quasi-experiments. There are three possible situations. The instrumental variable, the regression discontinuity design and the interrupted time series.

Each speaker will cover one of these situations focussing on the applied aspects. It is possible to find similar situations in a number of data sets ranging from epidemiology, public health and social sciences. 

Hopefully participants will find that they too are working with data sources where one of the above situations can be applied.

14:45-14:55 Registration
14:55-15:00 Welcome and motivation (Sara Geneletti)
15:00-15:45 Mendelian Randomisation for Causal Inference in Epidemiology (Vanessa Didelez)
15:45-16:30 The Regression Discontinuity Design in Social Science (Dominik Hangartner)

16:30-16:45 Break

16:45-17:30 Interrupted time series analysis in Public Health policy evaluation (Ioannis Bakolis)
17:30-17:45 Q&A for speakers

Mendelian Randomisation for Causal Inference in Epidemiology Watch video (YouTube)

Vanessa Didelez (Reader in Statistics, School of Mathematics, University of Bristol, UK)

In epidemiology, we are often faced with the problem that we can neither randomise exposure nor rule out unobserved confounding between the exposure and outcome of interest, e.g. alcohol consumption and coronary heart disease. Without any further information it is then impossible to draw credible conclusions about the causal effect of exposure. However, if one is a lucky, instances of Nature’s radmisation can be exploited. For example, people with a certain variant of the ALDH2 gene consume as good as no alcohol and can be regarded as a random sample of the population. As genes are randomly passed on from parents to offspring according to Mendel’s laws, we call this Mendelian randomisation and can statistically exploit such genetic data to draw causal conclusions. In economics the principle behind this approach is known as instrumental variable inference, the genetic variant(s) being the instrument(s) for the desired effect of exposure on outcome. In this presentation I will give an introduction to Mendelian randomisation, the underlying assumptions and some successful applications. I will address some recent challenges which are concerned with multiple instruments, i.e. the simultaneous use of more than one genetic variant, weak instruments, i.e. genetic variants that only weakly predict the exposure, non-linear models as one would typically want to use for binary outcomes, and how to deal with observed covariates.

The Regression Discontinuity Design in Social Science Watch video (YouTube)

Dominik Hangartner (Associate Professor, Department of Methodology at the London School of Economics and the Principal Investigator of the Migration Policy Lab at the University of Zurich)

This session will provide an applied introduction to both sharp and fuzzy regression discontinuity designs (RDD), discuss issues surrounding optimal bandwidth choice, and indirect tests of the underlying identification assumptions. Several applications will illustrate the applicability of the RDD in a variety of social science contexts.

Interrupted time series analysis in Public Health policy evaluation

Ioannis Bakolis (Lecturer in Biostatistics at the Department of Biostatistics and Department of Health Services and Population Research at King's College London)

Interrupted time series analysis in policy evaluation often compares event occurrence before and after a policy being implemented where differences between the two time periods may be indicative of the impact of the policy. However, apart from genuine policy effects, these differences could be a consequence of changes in other external factors such as temporal or unmeasured trends. This lecture will aim to provide an overall framework of two quasi-experimental designs for the analysis of routinely collected time series data: A regression discontinuity design to quantify the effect of smoke-free legislation on adverse birth outcomes in England; and a differences in differences approach for the analysis of small area data to evaluate whether waste incineration regulations to reduce incinerator emissions had a measureable impact on birth outcomes for mothers living close to a Municipal Waste Incinerator.

Contact Register by email to This email address is being protected from spambots. You need JavaScript enabled to view it.

Organiser Name Sara Geneletti

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