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Causal Diagrams For Empirical Research
"... The primary aim of this paper is to show how graphical models can be used as a mathematical language for integrating statistical and subjectmatter information. In particular, the paper develops a principled, nonparametric framework for causal inference, in which diagrams are queried to determine if ..."
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Cited by 180 (35 self)
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The primary aim of this paper is to show how graphical models can be used as a mathematical language for integrating statistical and subjectmatter information. In particular, the paper develops a principled, nonparametric framework for causal inference, in which diagrams are queried to determine if the assumptions available are sufficient for identifying causal effects from nonexperimental data. If so the diagrams can be queried to produce mathematical expressions for causal effects in terms of observed distributions; otherwise, the diagrams can be queried to suggest additional observations or auxiliary experiments from which the desired inferences can be obtained. Key words: Causal inference, graph models, interventions treatment effect 1 Introduction The tools introduced in this paper are aimed at helping researchers communicate qualitative assumptions about causeeffect relationships, elucidate the ramifications of such assumptions, and derive causal inferences from a combination...
On specifying graphical models for causation, and the identification problem
 Evaluation Review
, 2004
"... This paper (which is mainly expository) sets up graphical models for causation, having a bit less than the usual complement of hypothetical counterfactuals. Assuming the invariance of error distributions may be essential for causal inference, but the errors themselves need not be invariant. Graphs c ..."
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Cited by 18 (1 self)
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This paper (which is mainly expository) sets up graphical models for causation, having a bit less than the usual complement of hypothetical counterfactuals. Assuming the invariance of error distributions may be essential for causal inference, but the errors themselves need not be invariant. Graphs can be interpreted using conditional distributions, so that we can better address connections between the mathematical framework and causality in the world. The identification problem is posed in terms of conditionals. As will be seen, causal relationships cannot be inferred from a data set by running regressions unless there is substantial prior knowledge about the mechanisms that generated the data. There are few successful applications of graphical models, mainly because few causal pathways can be excluded on a priori grounds. The invariance conditions themselves remain to be assessed.
Alternating SubspaceSpanning Resampling to Accelerate Markov Chain Monte Carlo Simulation
, 2003
"... This article provides a simple method to accelerate Markov chain Monte Carlo sampling algorithms, such as the data augmentation algorithm and the Gibbs sampler, via alternating subspacespanning resampling (ASSR). The ASSR algorithm often shares the simplicity of its parent sampler but has dramatica ..."
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Cited by 8 (2 self)
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This article provides a simple method to accelerate Markov chain Monte Carlo sampling algorithms, such as the data augmentation algorithm and the Gibbs sampler, via alternating subspacespanning resampling (ASSR). The ASSR algorithm often shares the simplicity of its parent sampler but has dramatically improved efficiency. The methodology is illustrated with Bayesian estimation for analysis of censored data from fractionated experiments. The relationships between ASSR and existing methods are also discussed.
Principal stratification a goal or a tool? The
 International Journal of Biostatistics 7. Article
"... Principal stratification has recently become a popular tool to address certain causal inference questions particularly in dealing with postrandomization factors in randomized trials. Here we analyze the conceptual basis for this framework and invite response to clarify the value of principal strati ..."
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Cited by 7 (5 self)
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Principal stratification has recently become a popular tool to address certain causal inference questions particularly in dealing with postrandomization factors in randomized trials. Here we analyze the conceptual basis for this framework and invite response to clarify the value of principal stratification in estimating causal effects of interest.
Analysis of Interval Censored Data From Fractionated Experiments Using Covariance Adjustment
 Technometrics
, 2000
"... Censored data are commonly observed in industrial experiments such as for life testing and reliability improvement. Analyzing censored data from highly fractionated experiments presents a challenging problem to experimenters as many traditional methods become inadequate. Motivated by the data from a ..."
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Cited by 2 (2 self)
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Censored data are commonly observed in industrial experiments such as for life testing and reliability improvement. Analyzing censored data from highly fractionated experiments presents a challenging problem to experimenters as many traditional methods become inadequate. Motivated by the data from a fluorescentlamp experiment (Taguchi 1987; Hamada and Wu 1995), we consider in this paper analyzing censored data from highly fractionated experiments using covariance adjustment based on multivariate multiple regression models, which make use of the joint distribution of multivariate response variables. The Bayesian approach is taken for the main statistical inference in this paper. The posterior distribution of the parameters is obtained using the data augmentation (DA) algorithm (Tanner and Wong, 1987). We illustrate the methodology with the uorescentlamp experiment data. With the real example and a simulation study, we show that covariance adjustment can lead to both dramati...
Predictor Sort Sampling, Tight T's, and the Analysis of Covariance

, 1996
"... In this paper we revisit a method of sample allocation that has long been known to statisticians, and that has recently been "discovered" by wood strength researchers. The method allocates experimental units to blocks on the basis of the values of a variable, x, that is known to be correlated with t ..."
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Cited by 1 (0 self)
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In this paper we revisit a method of sample allocation that has long been known to statisticians, and that has recently been "discovered" by wood strength researchers. The method allocates experimental units to blocks on the basis of the values of a variable, x, that is known to be correlated with the response, y. We call this allocation method "predictor sort sampling." We demonstrate that the associated paired T analysis recommended in statistical texts is deficient if the sample size is small and the correlation between x and y is high. We temper this criticism of standard statistical intuition with a proof that the approach is asymptotically correct. In a related development we show that a modified pooled T approach can be taken to this data with a resultant increase in power. We compare these approaches to an analysis of covariance approach, and discuss the advantages of each. Finally, we warn against the intuitively attractive, but incorrect power calculations that are likely to...
Another Look at Among and Within Class Regressions in Analysis of Covariance
, 1989
"... In a recent paper, Monlezun and Blouin (1988) proposed an analysis of covariance for a split plot experiment. They suggest a model consisting of five regression coefficients, one for each of the effects wholeplot treatment means, wholeplot errors, split plottreatment means, wholeplot and split ..."
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In a recent paper, Monlezun and Blouin (1988) proposed an analysis of covariance for a split plot experiment. They suggest a model consisting of five regression coefficients, one for each of the effects wholeplot treatment means, wholeplot errors, split plottreatment means, wholeplot and splitplot treatment interactions and the experimental errors. In this paper we critically examine the analysis suggested by Monlezun and Blouin (1988). We relate their analysis to the within class (internal) and among class (external) regressions considered by Wishart and Sanders (1934) and Smith (1957) and present some of the issues that are not usually addressed in text books. Page 2 1.
Notice to Readers Review of Potentially Applicable Approaches to Benthic Invertebrate Data Analysis and Interpretation
"... The Aquatic Effects Technology Evaluation (AETE) program was established to review appropriate technologies for assessing the impacts of mine effluents on the aquatic environment. AETE is a cooperative program between the Canadian mining industry, several federal government departments and a number ..."
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The Aquatic Effects Technology Evaluation (AETE) program was established to review appropriate technologies for assessing the impacts of mine effluents on the aquatic environment. AETE is a cooperative program between the Canadian mining industry, several federal government departments and a number of provincial governments; it is coordinated by the Canada Centre for Mineral and Energy Technology (CANMET). The program was designed to be of direct benefit to the industry, and to government. Through technical and field evaluations, it identified costeffective technologies to meet environmental monitoring requirements. The program included three main areas: acute and sublethal toxicity testing, biological monitoring in receiving waters, and water and sediment monitoring. The technical evaluations are conducted to document certain tools selected by AETE members, and to provide the rationale for doing a field evaluation of the tools or provide specific guidance on field application of a method. In some cases, the technical evaluations included a go/no go recommendation that AETE takes into consideration before a field evaluation of a given method is conducted. The technical evaluations are published although they do not necessarily reflect the views of the
Benthic Invertebrate Data Analysis and Interpretation
"... The Aquatic Effects Technology Evaluation (AETE) program was established to review appropriate technologies for assessing the impacts of mine effluents on the aquatic environment. AETE is a cooperative program between the Canadian mining industry, several federal government departments and a number ..."
Abstract
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The Aquatic Effects Technology Evaluation (AETE) program was established to review appropriate technologies for assessing the impacts of mine effluents on the aquatic environment. AETE is a cooperative program between the Canadian mining industry, several federal government departments and a number of provincial governments; it is coordinated by the Canada Centre for Mineral and Energy Technology (CANMET). The program was designed to be of direct benefit to the industry, and to government. Through technical and field evaluations, it identified costeffective technologies to meet environmental monitoring requirements. The program included three main areas: acute and sublethal toxicity testing, biological monitoring in receiving waters, and water and sediment monitoring. The technical evaluations are conducted to document certain tools selected by AETE members, and to provide the rationale for doing a field evaluation of the tools or provide specific guidance on field application of a method. In some cases, the technical evaluations included a go/no go recommendation that AETE takes into consideration before a field evaluation of a given method is conducted. The technical evaluations are published although they do not necessarily reflect the views of the participants in the AETE Program. The technical evaluations should be considered as working documents rather than comprehensive literature reviews. The purpose of the technical evaluations was to document specific
JASA jasa v.2003/07/03 Prn:12/08/2003; 17:00 F:jasatm02159r3.tex; (Ramune) p. 1 Methodology for Evaluating a Partially Controlled Longitudinal Treatment Using Principal Stratification, With Application to a Needle Exchange Program
"... We consider studies for evaluating the shortterm effect of a treatment of interest on a timetoevent outcome. The studies we consider are only partially controlled in the following sense: (1) Subjects ’ exposure to the treatment of interest can vary over time, but this exposure is not directly con ..."
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We consider studies for evaluating the shortterm effect of a treatment of interest on a timetoevent outcome. The studies we consider are only partially controlled in the following sense: (1) Subjects ’ exposure to the treatment of interest can vary over time, but this exposure is not directly controlled by the study; (2) subjects ’ followup time is not directly controlled by the study; and (3) the study directly controls another factor that can affect subjects ’ exposure to the treatment of interest as well as subjects ’ followup time. When factors 1 and 2 are both present in the study, evaluating the treatment of interest using standard methods, including instrumental variables, does not generally estimate treatment effects. We develop the methodology for estimating the effect of treatment 1 in this setting of partially controlled studies under explicit assumptions using the framework for principal stratification for causal inference. We illustrate our methods by a study to evaluate the efficacy of the Baltimore Needle Exchange Program to reduce the risk of human immunodeficiency virus (HIV) transmission, using data on distance of the program’s sites from the subjects. KEY WORDS: