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188
DecisionTheoretic Foundations for Causal Reasoning
 Journal of Artificial Intelligence Research
, 1995
"... We present a definition of cause and effect in terms of decisiontheoretic primitives and thereby provide a principled foundation for causal reasoning. Our definition departs from the traditional view of causation in that causal assertions may vary with the set of decisions available. We argue that ..."
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Cited by 51 (8 self)
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We present a definition of cause and effect in terms of decisiontheoretic primitives and thereby provide a principled foundation for causal reasoning. Our definition departs from the traditional view of causation in that causal assertions may vary with the set of decisions available. We argue that this approach provides added clarity to the notion of cause. Also in this paper, we examine the encoding of causal relationships in directed acyclic graphs. We describe a special class of influence diagrams, those in canonical form, and show its relationship to Pearl's representation of cause and effect. Finally, we show how canonical form facilitates counterfactual reasoning. 1. Introduction Knowledge of cause and effect is crucial for modeling the affects of actions. For example, if we observe a statistical correlation between smoking and lung cancer, we can not conclude from this observation alone that our chances of getting lung cancer will change if we stop smoking. If, however, we als...
Railroads of the Raj: Estimating the Impact of Transportation Infrastructure
"... How large are the benefits of transportation infrastructure projects, and what explains these benefits? To shed new light on these questions, I collect archival data from colonial India and use it to estimate the impact of India’s vast railroad network. Guided by six predictions from a general equil ..."
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Cited by 44 (4 self)
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How large are the benefits of transportation infrastructure projects, and what explains these benefits? To shed new light on these questions, I collect archival data from colonial India and use it to estimate the impact of India’s vast railroad network. Guided by six predictions from a general equilibrium trade model, I find that railroads: (1) decreased trade costs and interregional price gaps; (2) increased interregional and international trade; (3) eliminated the responsiveness of local prices to local productivity shocks (but increased the transmission of these shocks between regions); (4) increased the level of real income (but harmed neighboring regions without railroad access); (5) decreased the volatility of real income; and that (6), a sufficient statistic for the effect of railroads on welfare in the model accounts for virtually all of the observed reducedform impact of railroads on real income. I find similar results from an instrumental variable specification, no spurious effects from over 40,000 km of lines that were approved but never built, and tight bounds on the estimated impact of railroads. These results suggest that transportation infrastructure projects can improve welfare significantly,
Direct and indirect causal effects via potential outcomes
 Scandinavian Journal of Statistics
, 2004
"... ..."
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 37 (1 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.
Statistical Treatment Rules for Heterogeneous Populations
, 2004
"... An important objective of empirical research on treatment response is to provide decision makers with information useful in choosing treatments. This paper studies minimaxregret treatment choice using the sample data generated by a classical randomized experiment. Consider a utilitarian social plan ..."
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Cited by 36 (6 self)
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An important objective of empirical research on treatment response is to provide decision makers with information useful in choosing treatments. This paper studies minimaxregret treatment choice using the sample data generated by a classical randomized experiment. Consider a utilitarian social planner who must choose among the feasible statistical treatment rules, these being functions that map the sample data and observed covariates of population members into a treatment allocation. If the planner knew the population distribution of treatment response, the optimal treatment rule would maximize mean welfare conditional on all observed covariates. The appropriate use of covariate information is a more subtle matter when only sample data on treatment response are available. I consider the class of conditional empirical success rules; that is, rules assigning persons to treatments that yield the best experimental outcomes conditional on alternative subsets of the observed covariates. I derive a closedform bound on the maximum regret of any such rule. Comparison of the bounds for rules that conditional on smaller and larger subsets of the covariates yields sufficient sample sizes for productive use of covariate information. When the available sample size exceeds the sufficiency boundary, a planner can be certain that conditioning treatment choice on more covariates is preferable (in terms of minimax regret) to conditioning on fewer covariates.
Propensity Score Estimation with Boosted Regression for Evaluating Causal Effects in Observational Studies
 Psychological Methods
, 2004
"... Causal effect modeling with naturalistic rather than experimental data is challenging. In observational studies participants in different treatment conditions may also differ on pretreatment characteristics that influence outcomes. Propensity score methods can theoretically eliminate these confounds ..."
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Cited by 36 (4 self)
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Causal effect modeling with naturalistic rather than experimental data is challenging. In observational studies participants in different treatment conditions may also differ on pretreatment characteristics that influence outcomes. Propensity score methods can theoretically eliminate these confounds for all observed covariates, but accurate estimation of propensity scores is impeded by large numbers of covariates, uncertain functional forms for their associations with treatment selection, and other problems. This paper demonstrates that boosting, a modern statistical technique, can overcome many of these obstacles. We illustrate this approach with a study of adolescent probationers in substance abuse treatment programs. Propensity score weights estimated using boosting eliminate most pretreatment group differences, and substantially alter the apparent relative effects of adolescent substance abuse treatment. Experimental studies offer the most rigorous evidence with which to establish treatment efficacy, but they are not always practical or feasible. Experimental treatment evaluations can be expensive to field and may be too slow to produce answers to pressing questions. In some cases
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 29 (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
www.hicn.org The Consequences of Child Soldiering
, 2007
"... In many cases, up to a third of male youth (including children) are drawn into armed groups, making soldiering one of the world’s most common occupations for the young. Little is known, however, about the impacts of military service on human capital and labor market outcomes due to an absence of dat ..."
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Cited by 25 (4 self)
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In many cases, up to a third of male youth (including children) are drawn into armed groups, making soldiering one of the world’s most common occupations for the young. Little is known, however, about the impacts of military service on human capital and labor market outcomes due to an absence of data as well as sample selection: recruits are usually selfselected and screened, and may also selectively survive. We assess the impacts of participation in civil war using an original survey from Uganda, where a rebel group’s recruitment method provides arguably exogenous variation in conscription. Contrary to the prevailing view that participation in war leads to broadbased ‘traumatization’, we find that military service primarily hinders longterm economic performance because it is a poor substitute for civilian education and work experience. The most significant impact is upon a recruit’s skills and productivity: schooling falls by nearly a year, skilled employment halves, and earnings drop by a third. These impacts are highly robust to relaxation of the assumption of exogenous conscription. Effects are greatest for child soldiers, who lose the most education. There is no observed impact on social capital, and adverse impacts
THE SCIENTIFIC MODEL OF CAUSALITY
, 2005
"... Causality is a very intuitive notion that is difficult to make precise without lapsing into tautology. Two ingredients are central to any definition: (1) a set of possible outcomes (counterfactuals) generated by a function of a set of ‘‘factors’ ’ or ‘‘determinants’ ’ and (2) a manipulation where on ..."
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Cited by 23 (2 self)
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Causality is a very intuitive notion that is difficult to make precise without lapsing into tautology. Two ingredients are central to any definition: (1) a set of possible outcomes (counterfactuals) generated by a function of a set of ‘‘factors’ ’ or ‘‘determinants’ ’ and (2) a manipulation where one (or more) of the ‘‘factors’ ’ or ‘‘determinants’’ is changed. An effect is realized as a change in the argument of a stable function that produces the same change in the outcome for a class of interventions that change the ‘‘factors’ ’ by the same amount. The outcomes are compared at different levels of the factors or generating variables. Holding all factors save one at a constant level, the change in the outcome associated with manipulation of the varied factor is called a causal effect of the manipulated factor. This definition, or some version of it, goes back to Mill (1848) and Marshall (1890). Haavelmo’s (1943) made it more precise within the context of linear equations models. The phrase ‘ceteris paribus’ (everything else held constant) is a mainstay of economic analysis
Inference and Hierarchical Modeling in the Social Sciences
, 1995
"... this paper I (1) examine three levels of inferential strength supported by typical social science datagathering methods, and call for a greater degree of explicitness, when HMs and other models are applied, in identifying which level is appropriate; (2) reconsider the use of HMs in school effective ..."
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Cited by 22 (6 self)
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this paper I (1) examine three levels of inferential strength supported by typical social science datagathering methods, and call for a greater degree of explicitness, when HMs and other models are applied, in identifying which level is appropriate; (2) reconsider the use of HMs in school effectiveness studies and metaanalysis from the perspective of causal inference; and (3) recommend the increased use of Gibbs sampling and other Markovchain Monte Carlo (MCMC) methods in the application of HMs in the social sciences, so that comparisons between MCMC and betterestablished fitting methodsincluding full or restricted maximum likelihood estimation based on the EM algorithm, Fisher scoring or iterative generalized least squaresmay be more fully informed by empirical practice.