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103
Nonparametric Analysis Of Randomized Experiments With Missing Covariate And Outcome Data
"... Analysis of randomized experiments with missing covariate and outcome data is problematic because the population parameters of interest are not identified unless one makes untestable assumptions about the distribution of the missing data. This paper shows how population parameters can be bounded wit ..."
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Cited by 27 (3 self)
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Analysis of randomized experiments with missing covariate and outcome data is problematic because the population parameters of interest are not identified unless one makes untestable assumptions about the distribution of the missing data. This paper shows how population parameters can be bounded without making untestable distributional assumptions. Bounds are also derived under the assumption that covariate data are missing completely at random. In each case the bounds are sharp; they exhaust all of the information that is available given the data and the maintained assumptions. The bounds are illustrated with applications to data obtained from a clinical trial and data relating family structure to the probability that a youth graduates from high school. Key Words: Identification, attrition, bounds We thank William G. Henderson, Domenic Reda, and David Williams of the Edward Hines, Jr., Hospital, U.S. Department of Veterans Affairs Cooperative Studies Program Coordinating Center, Hines...
Trimming for Bounds on Treatment Effects with Missing Outcomes
, 2002
"... for helpful discussions and suggestions. The views expressed in this paper are those of the author and not ..."
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Cited by 23 (6 self)
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for helpful discussions and suggestions. The views expressed in this paper are those of the author and not
Causal inference in statistics: An Overview
, 2009
"... This review presents empirical researcherswith recent advances in causal inference, and stresses the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underly all ca ..."
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Cited by 23 (8 self)
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This review presents empirical researcherswith recent advances in causal inference, and stresses the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underly all causal inferences, the languages used in formulating those assumptions, the conditional nature of all causal and counterfactual claims, and the methods that have been developed for the assessment of such claims. These advances are illustrated using a general theory of causation based on the Structural Causal Model (SCM) described in Pearl (2000a), which subsumes and unifies other approaches to causation, and provides a coherent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring (from a combination of data and assumptions) answers to three types of causal queries: (1) queries about the effects of potential interventions, (also called “causal effects ” or “policy evaluation”) (2) queries about probabilities of counterfactuals, (including assessment of “regret, ” “attribution” or “causes of effects”) and (3) queries about direct and indirect effects (also known as “mediation”). Finally, the paper defines the formal and conceptual relationships between the structural and potentialoutcome frameworks and presents tools for a symbiotic analysis that uses the strong features of both.
Moving the Goalposts: Addressing Limited Overlap in Estimation of Average Treatment Effects by Changing the Estimand
, 2004
"... Estimation of average treatment effects under unconfoundedness or selection on observables is often hampered by lack of overlap in the covariate distributions. This lack of overlap can lead to imprecise estimates and can make commonly used estimators sensitive to the choice of specification. In this ..."
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Cited by 20 (2 self)
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Estimation of average treatment effects under unconfoundedness or selection on observables is often hampered by lack of overlap in the covariate distributions. This lack of overlap can lead to imprecise estimates and can make commonly used estimators sensitive to the choice of specification. In this paper we develop formal methods for addressing such lack of overlap in which we sacrifice some external validity in exchange for improved internal validity. We characterize optimal subsamples where the average treatment effect can be estimated most precisely, as well optimally weighted average treatment effects. We show the problem of lack of overlap has important links to the presence of treatment effect heterogeneity: under the assumption of constant conditional average treatment effects (conditional on covariates) the treatment effect can be estimated much more precisely. The efficient estimator for the treatment effect under the assumption of a constant conditional average treatment effect is shown to be identical to the efficient estimator for the optimally weighted average treatment effect. We also develop tests for the null hypotheses of a constant and a zero conditional average treatment effect. The latter is shown to be much more powerful than
2009): “Set Identification in Models with Multiple Equilibria,” Working Paper, Université de Montreal
"... Abstract. We propose a computationally feasible way of deriving the identified set of parameter values in models with multiple equilibria, with particular emphasis on oligopoly entry models. This is achieved through an equivalence result between the existence of an equilibrium selection mechanism co ..."
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Cited by 18 (2 self)
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Abstract. We propose a computationally feasible way of deriving the identified set of parameter values in models with multiple equilibria, with particular emphasis on oligopoly entry models. This is achieved through an equivalence result between the existence of an equilibrium selection mechanism compatible with the observed data and a set of inequalities, and through an appeal to efficient linear programming techniques.
Instruments for Causal Inference  An Epidemiologist’s Dream?
, 2006
"... The use of instrumental variable (IV) methods is attractive because, even in the presence of unmeasured confounding, such methods may consistently estimate the average causal effect of an exposure on an outcome. However, for this consistent estimation to be achieved, several strong conditions must h ..."
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Cited by 18 (0 self)
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The use of instrumental variable (IV) methods is attractive because, even in the presence of unmeasured confounding, such methods may consistently estimate the average causal effect of an exposure on an outcome. However, for this consistent estimation to be achieved, several strong conditions must hold. We review the definition of an instrumental variable, describe the conditions required to obtain consistent estimates of causal effects, and explore their implications in the context of a recent application of the instrumental variables approach. We also present (1) a description of the connection between 4 causal models—counterfactuals, causal directed acyclic graphs, nonparametric structural equation models, and linear structural equation models—that have been used to describe instrumental variables methods; (2) a unified presentation of IV methods for the average causal effect in the study population through structural mean models; and (3) a discussion and new
Causal Inference from Indirect Experiments
, 1995
"... Indirect experiments are studies in which randomized control is replaced by randomized encouragement, that is, subjects are encouraged, rather than forced to receive treatment programs. The purpose of this paper is to bring to the attention of experimental researchers simple mathematical results tha ..."
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Cited by 15 (4 self)
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Indirect experiments are studies in which randomized control is replaced by randomized encouragement, that is, subjects are encouraged, rather than forced to receive treatment programs. The purpose of this paper is to bring to the attention of experimental researchers simple mathematical results that enable us to assess, from indirect experiments, the strength with which causal influences operate among variables of interest. The results reveal that despite the laxity of the encouraging instrument, indirect experimentation can yield significant and sometimes accurate information on the impact of a program on the population as a whole, as well as on the particular individuals who participated in the program. Keywords: Causal reasoning, treatment evaluation, noncompliance, graphical models 1 Introduction Standard experimental studies in the biological, medical, and behavioral sciences invariably invoke the instrument of randomized control, that is, subjects are assigned at random to va...
Nonparametric Bounds on Causal Effects from Partial Compliance Data
 JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
, 1993
"... Experimental studies in which treatment assignment is random but subject compliance is imperfect may be susceptible to bias; the actual effect of the treatment may deviate appreciably from the mean difference between treated and untreated subjects. This paper establishes universal formulas that can ..."
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Cited by 14 (10 self)
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Experimental studies in which treatment assignment is random but subject compliance is imperfect may be susceptible to bias; the actual effect of the treatment may deviate appreciably from the mean difference between treated and untreated subjects. This paper establishes universal formulas that can be used to bound the actual treatment effect in any experiment for which compliance data is available and in which the assignment influences the response only through the treatment given. Using a linear programming analysis, we present formulas that provide the tightest bounds that can be inferred on the average treatment effect, given an empirical distribution of assignments, treatments, and responses. The application of these results is demonstrated on data that relates cholesterol levels to cholestyramine treatment ([Lipid Research Clinic Program 84]).
Statistics and Causal Inference: A Review
, 2003
"... This paper aims at assisting empirical researchers benefit from recent advances in causal inference. The paper stresses the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assump ..."
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Cited by 12 (6 self)
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This paper aims at assisting empirical researchers benefit from recent advances in causal inference. The paper stresses the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underly all causal inferences, the languages used in formulating those assumptions, and the conditional nature of causal claims inferred from nonexperimental studies. These emphases are illustrated through a brief survey of recent results, including the control of confounding, the assessment of causal effects, the interpretation of counterfactuals, and a symbiosis between counterfactual and graphical methods of analysis.
Railroads of the Raj: Estimating the Economic Impact of Transportation Infrastructure,”mimeo
, 2008
"... I estimate the economic impact of the construction of colonial India’s railroad network from 18611930. Using newly collected districtlevel data on output, prices, rainfall, and intra and international trade flows I estimate that the railroad network had the following effects: (1) Railroads caused ..."
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Cited by 12 (1 self)
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I estimate the economic impact of the construction of colonial India’s railroad network from 18611930. Using newly collected districtlevel data on output, prices, rainfall, and intra and international trade flows I estimate that the railroad network had the following effects: (1) Railroads caused transport costs along optimal routes (according to a network flow algorithm) to fall by 73 percent for an average shipment. (2) The lower transport costs caused by railroads significantly increased India’s interregional and international trade. (3) The responsiveness of a region’s agricultural prices to its own rainfall shocks fell sharply after it was connected to the railroad network, but its responsiveness to shocks in other regions on the railroad network rose. (4) Railroad lines raised the level of real agricultural income by 18 percent in the districts in which they were built. I find similar results using rainfall shortages in the 187778 agricultural year as an instrumental variable for railroad construction post1880 (a response by the 1880 British parliament to the 1878 famine). And I find no effect in a variety of ‘placebo ’ specifications that use