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We extend the standard evaluation framework to allow for interactions between individuals within segmented markets. An individual's outcome depends not only on the assigned treatment status but also on (features of) the distribution of the assigned treatments in his market. To evaluate how the...
Persistent link: https://www.econbiz.de/10013148327
This paper assesses the effectiveness of unconfoundedness-based estimators of mean effects for multiple or multivalued treatments in eliminating biases arising from nonrandom treatment assignment. We evaluate these multiple treatment estimators by simultaneously equalizing average outcomes among...
Persistent link: https://www.econbiz.de/10013155543
-nearest neighbour (KNN) matching estimators of average causal effects. This is an interesting result showing that bootstrap should not … valid inference for KNN matching estimators. We resample "estimated individual causal effects" (EICE), i.e. the difference … respect to the matching covariate, we obtain a bootstrap scheme valid also with heterogeneous causal effects where mild …
Persistent link: https://www.econbiz.de/10013135182
selection-on-observables type assumptions using matching or propensity score methods. Much of this literature is highly …
Persistent link: https://www.econbiz.de/10013056251
Choosing among a number of available treatments the most suitable for a given subject is an issue of everyday concern. A physician has to choose an appropriate drug treatment or medical treatment for a given patient, based on a number of observed covariates X and prior experience. A case worker...
Persistent link: https://www.econbiz.de/10012779818
In this paper we study doubly robust estimators of various average treatment effects under unconfoundedness. We unify and extend much of the recent literature by providing a very general identification result which covers binary and multi-valued treatments; unnormalized and normalized weighting;...
Persistent link: https://www.econbiz.de/10013055561
A growing literature on inference in difference-in-differences (DiD) designs with grouped errors has been pessimistic about obtaining hypothesis tests of the correct size, particularly with few groups. We provide Monte Carlo evidence for three points: (i) it is possible to obtain tests of the...
Persistent link: https://www.econbiz.de/10013061928
Since the late 90s, Regression Discontinuity (RD) designs have been widely used to estimate Local Average Treatment Effects (LATE). When the running variable is observed with continuous measurement error, identification fails. Assuming non-differential measurement error, we propose a consistent...
Persistent link: https://www.econbiz.de/10012955015
When the running variable in a regression discontinuity (RD) design is measured with error, identification of the local average treatment effect of interest will typically fail. While the form of this measurement error varies across applications, in many cases the measurement error structure is...
Persistent link: https://www.econbiz.de/10012868822
This paper develops a novel wild bootstrap procedure to construct robust bias-corrected (RBC) valid confidence intervals (CIs) for fuzzy regression discontinuity designs, providing an intuitive complement to existing RBC methods. The CIs generated by this procedure are valid under conditions...
Persistent link: https://www.econbiz.de/10012858486