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Nonparametric estimation of average treatment effects under exogeneity: a review
 REVIEW OF ECONOMICS AND STATISTICS
, 2004
"... Recently there has been a surge in econometric work focusing on estimating average treatment effects under various sets of assumptions. One strand of this literature has developed methods for estimating average treatment effects for a binary treatment under assumptions variously described as exogen ..."
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Cited by 597 (26 self)
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Recently there has been a surge in econometric work focusing on estimating average treatment effects under various sets of assumptions. One strand of this literature has developed methods for estimating average treatment effects for a binary treatment under assumptions variously described as exogeneity, unconfoundedness, or selection on observables. The implication of these assumptions is that systematic (for example, average or distributional) differences in outcomes between treated and control units with the same values for the covariates are attributable to the treatment. Recent analysis has considered estimation and inference for average treatment effects under weaker assumptions than typical of the earlier literature by avoiding distributional and functionalform assumptions. Various methods of semiparametric estimation have been proposed, including estimating the unknown regression functions, matching, methods using the propensity score such as weighting and blocking, and combinations of these approaches. In this paper I review the state of this
Bootstrap Tests for Distributional Treatment Effects in Instrumental Variable Models
 Journal of the American Statistical Association
, 2002
"... This article considers the problem of assessing the distributional consequence s of a treatment on some outcome variable of interest when treatment intake is (possibly) nonrandomized, but there is a binary instrument available for the researcher. Such a scenario is common in observational studies an ..."
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Cited by 157 (1 self)
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This article considers the problem of assessing the distributional consequence s of a treatment on some outcome variable of interest when treatment intake is (possibly) nonrandomized, but there is a binary instrument available for the researcher. Such a scenario is common in observational studies and in randomized experiments with imperfect compliance. One possible approach to this problem is to compare the counterfactual cumulative distribution functions of the outcome with and without the treatment. This article shows how to estimate these distributions using instrumental variable methods and a simple bootstrap procedure is proposed to test distributional hypotheses, such as equality of distributions, � rstorder and secondorder stochastic dominance. These tests and estimators are applied to the study of the effects of veteran status on the distribution of civilian earnings. The results show a negative effect of military service during the Vietnam era that appears to be concentrated on the lower tail of the distribution of earnings. Firstorder stochastic dominance cannot be rejected by the data.
Sensitivity to exogeneity assumptions in program evaluation
 American Economic Review
, 2003
"... In many empirical studies of the effect of social programs researchers assume that, conditional on a set of observed covariates, assignment to the treatment is exogenous or unconfounded (aka selection on observables). Often this assumption is not realistic, and researchers are concerned about the ..."
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Cited by 82 (2 self)
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In many empirical studies of the effect of social programs researchers assume that, conditional on a set of observed covariates, assignment to the treatment is exogenous or unconfounded (aka selection on observables). Often this assumption is not realistic, and researchers are concerned about the robustness of their results to departures from it. One approach (e.g., Charles Manski, 1990) is to entirely drop the exogeneity assumption and investigate what can be learned about treatment effects without it. With unbounded outcomes, and in the absence of alternative identifying assumptions, there are no restrictions on the set of possible values for average treatment effects. This does
Training Wages, and Sample Selection: Estimating Sharp Bounds on Treatment Effects.” Review of Economic Studies
"... This paper empirically assesses the wage effects of the Job Corps program, one of the largest federallyfunded job training programs in the United States. Even with the aid of a randomized experiment, the impact of a training program on wages is difficult to study because of sample selection, a perv ..."
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Cited by 27 (0 self)
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This paper empirically assesses the wage effects of the Job Corps program, one of the largest federallyfunded job training programs in the United States. Even with the aid of a randomized experiment, the impact of a training program on wages is difficult to study because of sample selection, a pervasive problem in applied microeconometric research. Wage rates are only observed for those who are employed, and employment status itself may be affected by the training program. This paper develops an intuitive trimming procedure for bounding average treatment effects in the presence of sample selection. In contrast to existing methods, the procedure requires neither exclusion restrictions nor a bounded support for the outcome of interest. Identification results, estimators, and their asymptotic distribution, are presented. The bounds suggest that the program raised wages, consistent with the notion that the Job Corps raises earnings by increasing human capital, rather than solely through encouraging work. The estimator is generally applicable to typical treatment evaluation problems in which there is nonrandom sample selection/attrition.
Southern Methodist University
, 2003
"... We propose a procedure for estimating the critical values of the extended KolmogorovSmirnov tests of First and Second Order Stochastic Dominance in the general Kprospect case. We allow for the observations to be serially dependent and, for the …rst time, we can accommodate general dependence among ..."
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We propose a procedure for estimating the critical values of the extended KolmogorovSmirnov tests of First and Second Order Stochastic Dominance in the general Kprospect case. We allow for the observations to be serially dependent and, for the …rst time, we can accommodate general dependence amongst the prospects which are to be ranked. Also, the prospects may be the residuals from certain conditional models, opening the way for conditional ranking. We also propose a test of Prospect Stochastic Dominance. Our method is based on subsampling and we show that the resulting tests are consistent and powerful against some N ¡1=2 local alternatives. We also propose some heuristic methods for selecting subsample size and demonstrate in simulations that they perform reasonably. We would like to thank three referees, Joel Horowitz, and Michael Wolf for some helpful comments.
COMPARABLE HISTORIES
"... A new form of matching—optimal balanced risk set matching—is applied in an observationa l study of a treatment, cystoscopy and hydrodistention, given in response to the symptoms of the chronic, nonlethal disease interstitial cystitis. When a patient receives the treatment at time t, that patient is ..."
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A new form of matching—optimal balanced risk set matching—is applied in an observationa l study of a treatment, cystoscopy and hydrodistention, given in response to the symptoms of the chronic, nonlethal disease interstitial cystitis. When a patient receives the treatment at time t, that patient is matched to another patient with a similar history of symptoms up to time t who has not received the treatment up to time t; this is risk set matching. By using a penalty function in integer programming in a new way, we force the marginal distributions of symptoms to be balanced in the matched treated and control groups. Among all balanced matchings, we pick the one that is optimal in the sense of minimizing the multivariate pretreatment covariate distance within matched pairs. Under a simple model for the treatment assignment mechanism, we study the sensitivity of the ndings to hidden biases. In particular, we show that a simple, conventiona l sensitivity analysis is appropriate with risk set matching when the time to treatment follows a proportional hazards model with a timedependent unobserved covariate.
1.1 The AAA Randomized Trial: Cardiac Damage
"... Anthracyclines are quite effective at curing certain cancers of childhood, but they may damage the heart. The ACEInhibitor After Anthracycline (AAA) study compared enalapril to placebo in a randomized trial in an effort to determine whether treatment with enalapril would preserve or improve cardia ..."
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Anthracyclines are quite effective at curing certain cancers of childhood, but they may damage the heart. The ACEInhibitor After Anthracycline (AAA) study compared enalapril to placebo in a randomized trial in an effort to determine whether treatment with enalapril would preserve or improve cardiac function among children previously treated with anthracylines. As is true in many clinical trials, patient compliance with the study protocol was imperfect; some children took less than the prescribed dose of enalapril or placebo. Most analytical procedures that acknowledge imperfect compliance do so at signi cant cost, abandoning the tight logic of random assignment. With noncompliance, assignment to enalapril or placebo is randomized, but the dose of enalapril actually received is not, and selfselection effects parallel to those in observational studies can exist and have been documented in some instances. Some researchers advocate adherence to the strict logic of randomization by reporting only, or else strongly emphasizing, the socalled “intenttotreat ” analysis, which makes no use of information about compliance. Other researchers report analyses that are not justi ed by random assignment and can be subject to substantial biases, such as “per protocol ” analyses or “treatment received ” analyses. Here we apply a recent proposal for randomization inference with an instrumental variable that uses randomization as the “reasoned basis for inference ” in Fisher’s phrase. We make no assumption that compliance is random; indeed, compliance may be severely biased. Importantly, the proposed analysis will nd a statistically signi cant effect of the treatment if and only if the intenttotreat analysis nds a signi cant effect; yet, unlike intenttotreat analysis, our analysis acknowledges that a patient assigned to a drug that he or she does not take will not receive the drug’s pharmacological bene ts.
NBER WORKING PAPER SERIES TRAINING, WAGES, AND SAMPLE SELECTION: ESTIMATING SHARP BOUNDS ON TREATMENT EFFECTS
, 2005
"... of the UC Berkeley Econometrics and Labor Lunches, for useful comments and suggestions. The views expressed herein are those of the author(s) and do not necessarily reflect the views of the National Bureau of Economic Research. ..."
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of the UC Berkeley Econometrics and Labor Lunches, for useful comments and suggestions. The views expressed herein are those of the author(s) and do not necessarily reflect the views of the National Bureau of Economic Research.
Conditional Cash Transfers
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