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The identification power of smoothness assumptions in models with counterfactual outcomes
Kim, Wooyoung; Kwon, Koohyun; Kwon, Soonwoo; Lee, Sokbae - 2014
for the average treatment response by imposing smoothness conditions alone, by combining them with monotonicity …
Persistent link: https://www.econbiz.de/10010368182
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Identifying effects of multivalued treatments
Lee, Sokbae; Salanié, Bernard - 2018
recently unordered monotonicity. We show how treatment effects can be identified in a more general class of models that allows …
Persistent link: https://www.econbiz.de/10011941484
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Nonparametric instrumental variable estimation
Wilhelm, Daniel; Chetverikov, Denis; Kim, Dongwoo - 2017
able to impose monotonicity of the estimated function. The use of such a shape restriction may signi cantly improve the … constrained estimator that imposes the monotonicity, on the other hand, signi cantly reduces variance by removing oscillations of …
Persistent link: https://www.econbiz.de/10011941509
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Causal inference in case-control studies
Jun, Sung Jae; Lee, Sokbae - 2020
out that the odds ratio is generally a sharp upper bound for counterfactual relative risk under some monotonicity …
Persistent link: https://www.econbiz.de/10012621096
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Gaussian transforms modeling and the estimation of distributional regression functions
Spady, Richard Henry; Stouli, Sami - 2020
likelihood formulation for their global estimation. We obtain solutions that satisfy the monotonicity property of conditional …
Persistent link: https://www.econbiz.de/10012621130
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Testing for stochastic monotonicity
Lee, Sokbae; Linton, Oliver; Whang, Yoon-Jae - 2008
We propose a test of the hypothesis of stochastic monotonicity. This hypothesis is of interest in many applications in …
Persistent link: https://www.econbiz.de/10010288448
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Dual regression
Spady, Richard Henry; Stouli, Sami - 2019
We propose dual regression as an alternative to the quantile regression process for the global estimation of conditional distribution functions under minimal assumptions. Dual regression provides all the interpretational power of the quantile regression process while avoiding the need for...
Persistent link: https://www.econbiz.de/10012146353