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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 ..."
Abstract
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Cited by 630 (25 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
Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score
, 2000
"... We are interested in estimating the average effect of a binary treatment on a scalar outcome. If assignment to the treatment is independent of the potential outcomes given pretreatment variables, biases associated with simple treatment-control average comparisons can be removed by adjusting for diff ..."
Abstract
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Cited by 416 (35 self)
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We are interested in estimating the average effect of a binary treatment on a scalar outcome. If assignment to the treatment is independent of the potential outcomes given pretreatment variables, biases associated with simple treatment-control average comparisons can be removed by adjusting
Causal Inference in Observational Studies with Non-Binary Treatments
, 2012
"... Propensity score methods have become a part of the standard toolkit for applied researchers who wish to ascertain causal effects from observational data. While they were originally developed for binary treatments, several researchers have proposed generalizations of the propensity score methodology ..."
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Propensity score methods have become a part of the standard toolkit for applied researchers who wish to ascertain causal effects from observational data. While they were originally developed for binary treatments, several researchers have proposed generalizations of the propensity score methodology
Jump or Kink? Identification of Binary Treatment Regression Discontinuity Design without the Discontinuity
, 2011
"... Standard Regression Discontinuity (RD) designs exploit a discontinuity (a jump) in the treatment probability to identify a local average treatment effect (LATE). RD identi-fication fails or is weak when there is no jump or the jump is small. Unlike regression kink design (RKD), which requires a cont ..."
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Cited by 2 (2 self)
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continuous treatment, this paper considers a binary treatment. This paper shows that without a jump, one can still identify a treatment effect utilizing a slope change (a kink) in the treatment probability. This paper provides weak and easily testable behavioral assumptions for identification based on a kink
TriMatch: An R Package for Propensity Score Matching of Non-binary Treatments
, 2013
"... The use of propensity score methods (Rosenbaum and Rubin, 1983) have become popular for estimating causal inferences in observational studies in medical research (Austin, 2008) and in the social sciences (Thoemmes and Kim, 2011). In most cases however, the use of propensity score methods have been c ..."
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confined to a single treatment. Several researchers have suggested using propensity score methods with multiple control groups, or to simply perform two separate analyses, one between treatment one and the control and another between treatment two and control. This paper introduces the TriMatch package
Instrumental variables for binary treatments with heterogeneous treatment effects: A simple exposition. Contributions to Economic Analysis & Policy, 3, Article 9
"... This note provides a simple exposition of what IV can and cannot estimate in a model with a binary treatment variable and heterogeneous treatment effects. It shows how linear IV is a misspecification of functional form and the reason why linear IV estimates for this model will always depend on the i ..."
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Cited by 10 (0 self)
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This note provides a simple exposition of what IV can and cannot estimate in a model with a binary treatment variable and heterogeneous treatment effects. It shows how linear IV is a misspecification of functional form and the reason why linear IV estimates for this model will always depend
binary
"... The Stata Journal (yyyy) vv, Number ii, pp. 1–21 Nonparametric bounds for the causal effect in a ..."
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The Stata Journal (yyyy) vv, Number ii, pp. 1–21 Nonparametric bounds for the causal effect in a
Estimation of Average Treatment Effects with Misclassification
- Econometrica
, 2003
"... This paper provides conditions for identification, and an associated estimator, of the average effect of a binary treatment or policy on a scalar outcome in models where treatment may be misclassified. ..."
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Cited by 45 (10 self)
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This paper provides conditions for identification, and an associated estimator, of the average effect of a binary treatment or policy on a scalar outcome in models where treatment may be misclassified.
Results 1 - 10
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126,280
