Identification of the Effects of Dynamic Treatments by Sequential Conditional Independence Assumptions
AbstractThis paper approaches the causal analysis of sequences of interventions from a potential outcome perspective. The identifying power of several different assumptions concerning the connection between the dynamic selection process and the outcomes of different sequences is discussed. The assumptions invoke different randomisation assumptions which are compatible with different selection regimes. Parametric forms are not involved. When participation in the sequences is decided every period depending on its success so far, the resulting endogeneity problem destroys nonparametric identification for many parameters of interest. However, some interesting dynamic forms of the average treatment effect are identified. As an empirical example for the application of this approach, we reexamine the effects of training programmes for the unemployed in West Germany.
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Bibliographic InfoPaper provided by Department of Economics, University of St. Gallen in its series University of St. Gallen Department of Economics working paper series 2005 with number 2005-17.
Length: 48 pages
Date of creation: Aug 2005
Date of revision:
Other versions of this item:
- Michael Lechner & Ruth Miquel, 2010. "Identification of the effects of dynamic treatments by sequential conditional independence assumptions," Empirical Economics, Springer, vol. 39(1), pages 111-137, August.
- C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
- C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
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