In: European Radiology, 2015, vol. 25, no. 11, p. 3405-3413
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In: Social Indicators Research, 2015, vol. 122, no. 1, p. 189-210
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In: European Spine Journal, 2015, vol. 24, no. 2, p. 358-368
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In: Empirical Economics, 2015, vol. 49, no. 1, p. 1-31
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In: International Journal of Colorectal Disease, 2015, vol. 30, no. 12, p. 1667-1675
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In: European Journal of Political Economy, 2021, vol. 67, p. 19 p
Natural disasters are good examples of catastrophic events that may affect vote decisions. In this study, we analyze how the occurrence of earthquakes changes voters' behavior at municipal elections and which channels drive this change, focusing in particular on the role of media exposure. We exploit data from 13,338 municipal electoral cycles where incumbents seek reelection between 1993 and...
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In: Journal of Educational and Behavioral Statistics, 2012, vol. 37, no. 3, p. 443-474
As any empirical method used for causal analysis, social experiments are prone to attrition which may flaw the validity of the results. This paper considers the problem of partially missing outcomes in experiments. Firstly, it systematically reveals under which forms of attrition - in terms of its relation to observable and/or unobservable factors - experiments do (not) yield causal...
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In: Journal of Business and Economic Statistics, 2019, p. 1-60
We propose a novel approach for causal mediation analysis based on changesin- changes assumptions restricting unobserved heterogeneity over time. This allows disentangling the causal effect of a binary treatment on a continuous outcome into an indirect effect operating through a binary intermediate variable (called mediator) and a direct effect running via other causal mechanisms. We identify...
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In: Journal of Econometric Methods, 2019, vol. 8, no. 1, p. 1-20
Using a sequential conditional independence assumption, this paper discusses fully nonparametric estimation of natural direct and indirect causal effects in causal mediation analysis based on inverse probability weighting. We propose estimators of the average indirect effect of a binary treatment, which operates through intermediate variables (or mediators) on the causal path between the...
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In: Journal of Business and Economic Statistics, 2019, vol. 37, no. 4, p. 710-720
We propose a difference-in-differences approach for disentangling a total treatment effect within specific subpopulations into a direct effect and an indirect effect operating through a binary mediating variable. Random treatment assignment along with specific common trend and effect homogeneity assumptions identify the direct effects on the always and never takers, whose mediator is not...
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