Abstracts
Speakers' abstracts will appear here.
Definitions of Causality
David R. Cox, Nuffield College, Oxford
Some of the definitions of causality are reviewed and their implications for the interpretation of statistical analyses outlined.
Causal Models for Dynamic Treatments with Survival Outcomes
Vanessa Didelez, University of Bristol
A dynamic treatment is a set of rules that assigns for each time point when a treatment decision needs to be made what this should be, given the patient's history so far. An example in the context of HIV studies is "start treatment when CD4 count first drops below 600". An optimal dynamic treatment is such a set of rules that optimises a criterion reflecting better health for the patient. I will address issues of time-dependent confounding, idenitification, g-computation and marginal structural models with particular attention to the case of survival outcomes. Dynamic marginal structural models are becoming popular for HIV studies mentioned above. The particular difficulty with survival outcomes is that marginal and conditional models are typically not compatible. I will discuss and compare approaches, as well as the issue of simulating from structural models; this leads to additional insight and understanding of the models used in this context.
1807: Economic shocks, conflict and the slave trade
James Fenske, Economics, Oxford
Suppression of the slave trade after 1807 increased the incidence of conflict between Africans. We use geo-coded data on African conflicts to uncover a discontinuous increase in conflict after 1807 in areas affected by the slave trade. In West Africa, the slave trade declined. This empowered interests that rivaled existing authorities, and political leaders resorted to violence in order to maintain their influence. In West-Central and South-East Africa, slave exports increased after 1807 and were produced through violence. We validate our explanation using Southwestern Nigeria and Eastern South Africa as examples.
Model Selection in the Presence of Many Correlated Instruments
Benjamin Frot, Department of Statistics, Oxford
(Joint work with Luke Jostins and Gil McVean)
Mendelian Randomisation
is an instrumental variable method that uses genetic variants as
instruments. In most cases, a very large number of putative
instruments is available but little is known about their
validity. Pleiotropy designates such a common situation in which a
single variant has an effect on multiple outcomes through different
mechanisms : this is a violation of the conditional independence
assumption required in instrumental variable analyses. Another issue
raised by genetic data sets comes from the large number of observed
variables (e.g. ~23K in the case of gene networks) along with big
sample sizes. I will first reason about a simple example involving a
Gaussian graphical model with two instruments and two observed
variables in order to show how Bayesian methods can be leveraged to
deal with likelihood nonidentifiability and pleiotropy. I will then
extend this analysis to larger systems and introduce an MCMC algorithm
that allows a principled random walk on the space of possible directed
acyclic graphs. In this context, a sparsity principle can be used to
deal with pleiotropy.
Causation in Sociology as a Population Science
John Goldthorpe, Sociol Policy and Intervention, Oxford
I consider three understandings of causation found in sociology: (1) as robust dependence; (2) as consequential manipulation; and (3) as generative process. I discuss problems that arise with (1) and (2) in sociology, and argue in favour of (3) as being that most appropriate for sociology considered as a population science. I give an illustration of (3) in the case of the explanation of 'secondary effects' in social inequalities in educational attainment.
Theory-independent limits on correlations from generalised Bayesian networks
Raymond Lal, Computer Science, Oxford
(Joint work with Joe Henson and Matt Pusey)
Quantum correlations which violate Bell inequalities cannot be
recovered using classical random variables that are assigned to a
certain background causal structure. Moreover, Tsirelson's bound
demonstrates a separation between quantum correlations and those
achievable by an arbitrary no-signalling theory. However, one can
consider causal structures other than that of the usual Bell setup. We
investigate quantum correlations from this more general perspective,
particularly in relation to Pearl's influential work on causality and
probabilistic reasoning, which uses the formalism of Bayesian
networks. We extend this formalism to the setting of generalised
probabilistic theories, and show that the classical d-separation
theorem extends to our setting. We also explore how classical, quantum
and general probabilistic theories separate for other causal
structures; and also when all three sets coincide.
Causal Inference through a Witness Protection Program
Ricardo Silva, Statistical Science, UCL
One of the most fundamental problems in causal inference is the
estimation of a causal effect when variables are confounded. In a
observational study, when no randomized trial is performed, this is
a difficult task as one has no direct evidence all confounders have
been adjusted for. We introduce a novel approach for estimating
causal effects that exploits observational conditional
independencies to suggest "weak" paths in a unknown causal
graph. The widely used faithfulness condition of Spirtes et al. is
relaxed to allow for a varying degree of "path cancellations" that
will imply conditional independencies but do not rule out the
existence of confounding causal paths. The outcome is a posterior
distribution over bounds on the average causal effect via a linear
programming approach and Bayesian inference. We claim this approach
should be used in practice along other default tools in
observational studies.
(Joint work with Robin Evans)