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From late to MTE : alternative methods for the evaluation of policy interventions
Cornelißen, Thomas; Dustmann, Christian; Raute, Anna; … - 2016
Persistent link: https://www.econbiz.de/10011524403
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Regression Discontinuity Design with Continuous Measurement Error in the Running Variable
Davezies, Laurent - 2017
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
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Proxy Variables and Nonparametric Identification of Causal Effects
de Luna, Xavier - 2016
Proxy variables are often used in linear regression models with the aim of removing potential confounding bias. In this paper we formalise proxy variables within the potential outcome framework, giving conditions under which it can be shown that causal effects are nonparametrically identified....
Persistent link: https://www.econbiz.de/10012986751
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Regression discontinuity design with continuous measurement error in the running variable
Davezies, Laurent; Le Barbanchon, Thomas - 2017
Persistent link: https://www.econbiz.de/10011619287
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Causal Effects of Monetary Shocks : Semiparametric Conditional Independence Tests with a Multinomial Propensity Score
Angrist, Joshua D.; Kuersteiner, Guido M. - 2021
Macroeconomists have long been concerned with the causal effects of monetary policy. When the identification of causal effects is based on a selection-on-observables assumption, non-causality amounts to the conditional independence of outcomes and policy changes. This paper develops a...
Persistent link: https://www.econbiz.de/10013325071
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The Finite Sample Performance of Semi- and Nonparametric Estimators for Treatment Effects and Policy Evaluation
Frölich, Markus - 2015
propensity scores, nonparametric regression, and direct covariate matching. In addition to (pair, radius, and kernel) matching … such as genetic matching, entropy balancing, and empirical likelihood estimation.We vary a range of features (sample size …. Nonparametric regression, nonparametric doubly robust estimation, nonparametric IPW, and one-to-many covariate matching perform best …
Persistent link: https://www.econbiz.de/10013029647
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Policy Evaluation Using Causal Inference Methods
Fougère, Denis - 2020
This chapter describes the main impact evaluation methods, both experimental and quasi-experimental, and the statistical model underlying them. Some of the most important methodological advances to have recently been put forward in this field of research are presented. We focus not only on the...
Persistent link: https://www.econbiz.de/10012843149
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Inference on Causal Effects in a Generalized Regression Kink Design
Card, David - 2015
We consider nonparametric identification and estimation in a nonseparable model where a continuous regressor of interest is a known, deterministic, but kinked function of an observed assignment variable. This design arises in many institutional settings where a policy variable (such as weekly...
Persistent link: https://www.econbiz.de/10013029646
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Sensitivity of matching-based program evaluations to the availability of control variables
Lechner, Michael; Wunsch, Conny - 2011
the question whether the omission of important control variables in matching estimation leads to biased impact estimates …
Persistent link: https://www.econbiz.de/10008989383
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Sensitivity of Matching-Based Program Evaluations to the Availability of Control Variables
Lechner, Michael - 2011
the question whether the omission of important control variables in matching estimation leads to biased impact estimates …
Persistent link: https://www.econbiz.de/10013128839