Micro-econometric and Micro-Macro linked models: Impact of the National Agricultural Advisory Services (NAADS) Program of Uganda—Considering different levels of likely contamination with the treatment
An important problem in causal inference and estimation of treatment effects is identifying a reliable comparison group (control observations) against which to compare those that have been exposed to the treatment (treated observations). It is common knowledge that the estimate obtained by the diffe...
| Autores principales: | , , , , , , |
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| Formato: | Capítulo de libro |
| Lenguaje: | Inglés |
| Publicado: |
Springer
2018
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/145426 |
| Sumario: | An important problem in causal inference and estimation of treatment effects is identifying a reliable comparison group (control observations) against which to compare those that have been exposed to the treatment (treated observations). It is common knowledge that the estimate obtained by the difference in the values of the indicator of interest associated with the two groups could be biased due to lack of overlap in the covariate distributions or common support between the treated and control observations (Dehejia and Wahba 2002; Imbens and Wooldridge 2009). This is especially problematic with non-experimental control observations (Dehejia and Wahba 2002) in which case combining propensity score matching and regression methods has been suggested to yield more consistent estimates of the treatment effect than using either method alone (Imbens and Wooldridge 2009). Matching removes self-selection bias due to any correlation between the observable (pre-treatment) covariates and the dependent variable, while regression isolates the effect of change in the covariates on change in the dependent variable over the period of the treatment. Using the combined approach, this paper discusses the effect of using different sets of control groups on estimates of treatment effects of the agricultural extension system in Uganda, the National Agricultural Advisory Services (NAADS) program. |
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