Causal Reasoning from Longitudinal Data*

Abstract. This paper reviews some of the key statistical ideas that are encountered when trying to find empirical support to causal interpretations and conclusions, by applying statistical methods on experimental or observational longitudinal data. In such data, typically a collection of individuals are followed over time, then each one has registered a sequence of covariate measurements along with values of control variables that in the analysis are to be interpreted as causes, and finally the individual outcomes or responses are reported. Particular attention is given to the potentially important problem of confounding. We provide conditions under which, at least in principle, unconfounded estimation of the causal effects can be accomplished. Our approach for dealing with causal problems is entirely probabilistic, and we apply Bayesian ideas and techniques to deal with the corresponding statistical inference. In particular, we use the general framework of marked point processes for setting up the probability models, and consider posterior predictive distributions as providing the natural summary measures for assessing the causal effects. We also draw connections to relevant recent work in this area, notably to Judea Pearl's formulations based on graphical models and his calculus of so‐called do‐probabilities. Two examples illustrating different aspects of causal reasoning are discussed in detail.     

On an adaptive version of the Metropolis–Hastings algorithm with independent proposal distribution

In this paper, we present a general formulation of an algorithm, the adaptive independent chain (AIC), that was introduced in a special context in Gåsemyr et al . [ Methodol. Comput. Appl. Probab. 3 (2001)]. The algorithm aims at producing samples from a specific target distribution Π, and is an adaptive, non-Markovian version of the Metropolis–Hastings independent chain. A certain parametric class of possible proposal distributions is fixed, and the parameters of the proposal distribution are updated periodically on the basis of the recent history of the chain, thereby obtaining proposals that get ever closer to Π. We show that under certain conditions, the algorithm produces an exact sample from Π in a finite number of iterations, and hence that it converges to Π. We also present another adaptive algorithm, the componentwise adaptive independent chain (CAIC), which may be an alternative in particular in high dimensions. The CAIC may be regarded as an adaptive approximation to the Gibbs sampler updating parametric approximations to the conditionals of Π.     

Modeling External Risks in Project Management

To ascertain the viability of a project, undertake resource allocation, take part in bidding processes, and other related decisions, modern project management requires forecasting techniques for cost, duration, and performance of a project, not only under normal circumstances, but also under external events that might abruptly change the status quo. We provide a Bayesian framework that provides a global forecast of a project's performance. We aim at predicting the probabilities and impacts of a set of potential scenarios caused by combinations of disruptive events, and using this information to deal with project management issues. To introduce the methodology, we focus on a project's cost, but the ideas equally apply to project duration or performance forecasting. We illustrate our approach with an example based on a real case study involving estimation of the uncertainty in project cost while bidding for a contract.     

Block external bootstrap in partially linear models with nonstationary strong mixing error terms

The authors extend the block external bootstrap to partially linear regression models with strongly mixing, nonstationary error terms. In addition to providing an approximate distribution for the semiparametric least square estimator of the parametric component, they propose a consistent estimator of the co‐variance matrix of this estimator.     

If you look like me,I'll talk to you: a preliminary study of ethnic identity and inter‐ethnic interaction among women in Singapore

In Singapore, government policy is for equal but separate development of the four major ethnic groups—Chinese, Malay, Indian and other. In this study, I attempt to gain some preliminary views of how strongly women identify with their own ethnic group and how freely they are prepared to interact with people from other ethnic groups in non‐work‐related situations. I confine my study to females for two reasons. One is that traditional ethnic dress is common among females in Singapore but much rarer among men, and this makes a strong non‐verbal statement of identity. The second reason is to avoid differences between males and females, which I did not wish to pursue within the limits of this exploratory study. The findings of my pilot investigation indicate that intra‐ethnic spontaneous interaction is more likely to occur among women who display a strong national identity. Moreover, younger women, who were exposed during their school years to the government's recent drive to nurture ethnic and cultural differences, are less open to inter‐ethnic interaction than are women in their 30s and older, who grew up when the government drive was towards creating one common national identity for the people of Singapore.     

Bayesian analysis of outlier problems using divergence measures

A Bayesian approach is presented for detecting influential observations using general divergence measures on the posterior distributions. A sampling-based approach using a Gibbs or Metropolis-within-Gibbs method is used to compute the posterior divergence measures. Four specific measures are proposed, which convey the effects of a single observation or covariate on the posterior. The technique is applied to a generalized linear model with binary response data, an overdispersed model and a nonlinear model. An asymptotic approximation using Laplace method to obtain the posterior divergence is also briefly discussed.     

Who Gets What From Government? Distributional Consequences of Child‐Care Assistance Policies

Given the fragmented structure of child‐care assistance in the United States, it has been difficult to obtain accurate estimates of which families are assisted, through which mechanisms, and at what level. Making use of survey data from New York City, we analyze the distribution of several forms of public child‐care assistance. Results suggest that about 40% of all families with young children receive some form of child‐care assistance. Considering all forms of assistance, the distribution of child‐care help is targeted in both expected and some unexpected ways. Implications of these results are discussed in the context of U.S. child‐care policies governing access and benefit levels.     

Modelling map positional error to infer true feature location

The authors consider the issue of map positional error, or the difference between location as represented in a spatial database (i.e., a map) and the corresponding unobservable true location. They propose a fully model‐based approach that incorporates aspects of the map registration process commonly performed by users of geographic informations systems, including rubber‐sheeting. They explain how estimates of positional error can be obtained, hence estimates of true location. They show that with multiple maps of varying accuracy along with ground truthing data, suitable model averaging offers a strategy for using all of the maps to learn about true location.     

Making the Best of a Bad Job? Female Part‐Timers’ Orientations and Attitudes to Work

This article uses data from 50 qualitative interviews with female part‐time workers in low‐level jobs in Britain to illustrate their attitudes, orientations and aspirations towards paid work. The research reports two main findings. Firstly, even female part‐time workers at the bottom end of the occupational structure are not a homogeneous group of workers. Secondly, they are not all highly satisfied with their jobs. The article argues that many women (especially those in working‐class households) still do not have a genuine choice between ‘family work’ and ‘market work’. It concludes by presenting a theoretical model of orientations to work.     

On the preservation of copula structure under truncation

The author characterizes the copula associated with the bivariate survival model of Clayton (1978) as the only absolutely continuous copula that is preserved under bivariate truncation.