628 research outputs found
Graphical Models for Inference Under Outcome-Dependent Sampling
We consider situations where data have been collected such that the sampling
depends on the outcome of interest and possibly further covariates, as for
instance in case-control studies. Graphical models represent assumptions about
the conditional independencies among the variables. By including a node for the
sampling indicator, assumptions about sampling processes can be made explicit.
We demonstrate how to read off such graphs whether consistent estimation of the
association between exposure and outcome is possible. Moreover, we give
sufficient graphical conditions for testing and estimating the causal effect of
exposure on outcome. The practical use is illustrated with a number of
examples.Comment: Published in at http://dx.doi.org/10.1214/10-STS340 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Identifying the consequences of dynamic treatment strategies: A decision-theoretic overview
We consider the problem of learning about and comparing the consequences of
dynamic treatment strategies on the basis of observational data. We formulate
this within a probabilistic decision-theoretic framework. Our approach is
compared with related work by Robins and others: in particular, we show how
Robins's 'G-computation' algorithm arises naturally from this
decision-theoretic perspective. Careful attention is paid to the mathematical
and substantive conditions required to justify the use of this formula. These
conditions revolve around a property we term stability, which relates the
probabilistic behaviours of observational and interventional regimes. We show
how an assumption of 'sequential randomization' (or 'no unmeasured
confounders'), or an alternative assumption of 'sequential irrelevance', can be
used to infer stability. Probabilistic influence diagrams are used to simplify
manipulations, and their power and limitations are discussed. We compare our
approach with alternative formulations based on causal DAGs or potential
response models. We aim to show that formulating the problem of assessing
dynamic treatment strategies as a problem of decision analysis brings clarity,
simplicity and generality.Comment: 49 pages, 15 figure
On Granger-causality and the effect of interventions in time series
We combine two approaches to causal reasoning. Granger-causality, on the one hand, is popular in fields like econometrics, where randomised experiments are not very common. Instead information about the dynamic development of a system is explicitly modelled and used to define potentially causal relations. On the other hand, the notion of causality as effect of interventions is predominant in fields like medical statistics or computer science. In this paper, we consider the effect of external, possibly multiple and sequential, interventions in a system of multivariate time series, the Granger-causal structure of which is taken to be known. We address the following questions: under what assumptions about the system and the interventions does Granger-causality inform us about the effectiveness of interventions, and when does the possibly smaller system of observable times series allow us to estimate this effect? For the latter we derive criteria that can be checked graphica lly and are in the same spirit as Pearl''s back-door and front-door criteria (Pearl 1995).econometrics;
Local independence graphs for composable Markov processes
The concept of local independence is used to define local independence graphs representing the dynamic dependence structure of several continuous time processes which jointly form a so-called composable Markov process. Specific properties of this new class of graphs are discussed such as the role of separating sets. Further insight is gained by considering possible extensions to the discrete time situation. It is shown that the latter case can be reduced to classical graphical interaction models
Assumptions of IV Methods for Observational Epidemiology
Instrumental variable (IV) methods are becoming increasingly popular as they
seem to offer the only viable way to overcome the problem of unobserved
confounding in observational studies. However, some attention has to be paid to
the details, as not all such methods target the same causal parameters and some
rely on more restrictive parametric assumptions than others. We therefore
discuss and contrast the most common IV approaches with relevance to typical
applications in observational epidemiology. Further, we illustrate and compare
the asymptotic bias of these IV estimators when underlying assumptions are
violated in a numerical study. One of our conclusions is that all IV methods
encounter problems in the presence of effect modification by unobserved
confounders. Since this can never be ruled out for sure, we recommend that
practical applications of IV estimators be accompanied routinely by a
sensitivity analysis.Comment: Published in at http://dx.doi.org/10.1214/09-STS316 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
A comparative analysis of graphical interaction and logistic regression modelling: self-care and coping with a chronic illness in later life
Quantitative research especially in the social, but also in the biological sciences has been limited by the availability and applicability of analytic techniques that elaborate interactions among behaviours, treatment effects, and mediating variables. This gap has been filled by a newly developed statistical technique, known as graphical interaction modelling. The merit of graphical models for analyzing highly structured data is explored in this paper by an empirical study on coping with a chronic condition as a function of interrelationships between three sets of factors. These include background factors, illness context factors and four self--care practices. Based on a graphical chain model, the direct and indirect dependencies are revealed and discussed in comparison to the results obtained from a simple logistic regression model ignoring possible interaction effects. Both techniques are introduced from a more tutorial point of view instead of going far into technical details
- ā¦