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271
Optimal Structural Nested Models for Optimal Sequential Decisions
 In Proceedings of the Second Seattle Symposium on Biostatistics
, 2004
"... ABSTRACT: I describe two new methods for estimating the optimal treatment regime (equivalently, protocol, plan or strategy) from very high dimesional observational and experimental data: (i) gestimation of an optimal doubleregime structural nested mean model (drSNMM) and (ii) gestimation of a sta ..."
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Cited by 60 (5 self)
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ABSTRACT: I describe two new methods for estimating the optimal treatment regime (equivalently, protocol, plan or strategy) from very high dimesional observational and experimental data: (i) gestimation of an optimal doubleregime structural nested mean model (drSNMM) and (ii) gestimation of a standard single regime SNMM combined with sequential dynamicprogramming (DP) regression. These methods are compared to certain regression methods found in the sequential decision and reinforcement learning literatures and to the regret modelling methods of Murphy (2003). I consider both Bayesian and frequentist inference. In particular, I propose a novel “Bayesfrequentist compromise ” that combines honest subjective non or semiparametric Bayesian inference with good frequentist behavior, even in cases where the model is so large and the likelihood function so complex that standard (uncompromised) Bayes procedures have poor frequentist performance. 1
Learning Probabilistic Networks
 THE KNOWLEDGE ENGINEERING REVIEW
, 1998
"... A probabilistic network is a graphical model that encodes probabilistic relationships between variables of interest. Such a model records qualitative influences between variables in addition to the numerical parameters of the probability distribution. As such it provides an ideal form for combini ..."
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Cited by 44 (2 self)
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A probabilistic network is a graphical model that encodes probabilistic relationships between variables of interest. Such a model records qualitative influences between variables in addition to the numerical parameters of the probability distribution. As such it provides an ideal form for combining prior knowledge, which might be limited solely to experience of the influences between some of the variables of interest, and data. In this paper, we first show how data can be used to revise initial estimates of the parameters of a model. We then progress to showing how the structure of the model can be revised as data is obtained. Techniques for learning with incomplete data are also covered.
Causal Inference for Complex Longitudinal Data: the continuous case
 Annals of Statistics
, 2001
"... this paper we consider two fundamental issues concerning Robins' theory. Firstly, do his assumed relations (between observed and unobservedfactual and counterfactualrandom variables) place restrictions on the distribution of the observed variables. If the answer is yes, adopting his appro ..."
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Cited by 42 (5 self)
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this paper we consider two fundamental issues concerning Robins' theory. Firstly, do his assumed relations (between observed and unobservedfactual and counterfactualrandom variables) place restrictions on the distribution of the observed variables. If the answer is yes, adopting his approach means making restrictive implicit assumptionsnot very desirable. If however the answer is no, his approach is neutral. One can freely use it in modelling and estimation, exploring the consequences (for the unobserved variables) of the model. This follows the highly succesful tradition in all sciences of making thought experiments. In what philosophical sense counterfactuals actually exist seems to us less relevant. But it is important to know if a certain thought experiment is a priori ruled out by existing data
Reasoning With Cause And Effect
, 1999
"... This paper summarizes basic concepts and principles that I have found to be useful in dealing with causal reasoning. The paper is written as a companion to a lecture under the same title, to be presented at IJCAI99, and is intended to supplement the lecture with technical details and pointers to mo ..."
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Cited by 40 (0 self)
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This paper summarizes basic concepts and principles that I have found to be useful in dealing with causal reasoning. The paper is written as a companion to a lecture under the same title, to be presented at IJCAI99, and is intended to supplement the lecture with technical details and pointers to more elaborate discussions in the literature. The ruling conception will be to treat causation as a computational schema devised to identify the invariant relationships in the environment, so as to facilitate reliable prediction of the effect of actions. This conception, as well as several of its satellite principles and tools, has been guiding paradigm for several research communities in AI, most notably those connected with causal discovery, troubleshooting, planning under uncertainty and modeling the behavior of physical systems. My hopes are to encourage a broader and more effective usage of causal modeling by explicating these common principles in simple and familiar mathematical form. Af...
A conceptual framework for adaptive preventive interventions
 Prevention Science
, 2004
"... Recently, adaptive interventions have emerged as a new perspective on prevention and treatment. Adaptive interventions resemble clinical practice in that different dosages of certain prevention or treatment components are assigned to different individuals, and/or within individuals across time, wi ..."
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Cited by 39 (0 self)
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Recently, adaptive interventions have emerged as a new perspective on prevention and treatment. Adaptive interventions resemble clinical practice in that different dosages of certain prevention or treatment components are assigned to different individuals, and/or within individuals across time, with dosage varying in response to the intervention needs of individuals. To determine intervention need and thus assign dosage, adaptive interventions use prespecified decision rules based on each participant’s values on key characteristics, called tailoring variables. In this paper, we offer a conceptual framework for adaptive interventions, discuss principles underlying the design and evaluation of such interventions, and review some areas where additional research is needed. KEY WORDS: adaptive interventions; prevention; research design. For most of the history of researchbased interventions aimed at prevention and treatment, the composition and dosage of these interventions have been fixed, in other words, a single composition and dosage has been offered to all program participants. For example, a schoolbased drug abuse prevention curriculum might be delivered to all sixth graders. Every component of the intervention that may be necessary for any particular participant is included in the curriculum, and each child is given the same intervention. Although it is recognized that individuals may have different intervention needs, it is expected that the intervention is in no way diluted or made counterproductive if components that are particularly relevant for an individual are combined with components that may have less, or even no, relevance for that individual.
On the identification of causal effects
, 2003
"... This paper deals with the problem of inferring causeeffect relationships from a combination of data and theoretical assumptions. This problem arises in diverse fields such as artificial intelligence, statistics, cognitive science, economics, and the health and social sciences. For example, investig ..."
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Cited by 32 (5 self)
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This paper deals with the problem of inferring causeeffect relationships from a combination of data and theoretical assumptions. This problem arises in diverse fields such as artificial intelligence, statistics, cognitive science, economics, and the health and social sciences. For example, investigators in the health sciences are
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 29 (2 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.
Alternative graphical causal models and the identification of direct effects
, 2009
"... We consider four classes of graphical causal models: the Finest Fully Randomized Causally ..."
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Cited by 29 (4 self)
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We consider four classes of graphical causal models: the Finest Fully Randomized Causally