Matching Estimating of Dynamic Treatment Models: Some Practical Issues
AbstractLechner and Miquel (2001) approached the causal analysis of sequences of interventions from a potential outcome perspective based on selection on observable type of assumptions (sequential conditional independence assumptions). Lechner (2004) proposed matching estimators for this framework. However, many practical issues that might have substantial consequences for interpretation of the results have not been thoroughly investigated so far. This paper discusses some of these practical issues. The discussion is related to estimates based on an artificial data set for which the true values of the parameters are known and that shares many features of data that could be used for an empirical dynamic matching analysis.
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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 2006 with number 2006-03.
Length: 39 pages
Date of creation: Jan 2006
Date of revision:
Dynamic treatment regimes; nonparametric identification; causal effects; sequential randomisation; programme evaluation; treatment effects; dynamic matching; panel data;
Find related papers by JEL classification:
- 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
- C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
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- Stefan Boes, 2009. "Partial Identification of Discrete Counterfactual Distributions with Sequential Update of Information," SOI - Working Papers 0918, Socioeconomic Institute - University of Zurich.
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