Abstract
This paper contributes to the emerging Bayesian literature on treatment effects. It derives treatment parameters in the framework of a potential outcomes model with a treatment choice equation, where the correlation between the unobservable components of the model is driven by a low-dimensional vector of latent factors. The analyst is assumed to have access to a set of measurements generated by the latent factors. This approach has attractive features from both theoretical and practical points of view. Not only does it address the fundamental identification problem arising from the inability to observe the same person in both the treated and untreated states, but it also turns out to be straightforward to implement. Formulae are provided to compute mean treatment effects as well as their distributional versions. A Monte Carlo simulation study is carried out to illustrate how the methodology can easily be applied.
| Original language | English |
|---|---|
| Journal | Econometric Reviews |
| Volume | 33 |
| Issue number | 1-4 |
| Pages (from-to) | 36-67 |
| ISSN | 0747-4938 |
| DOIs | |
| Publication status | Published - Feb 2014 |
Keywords
- Faculty of Social Sciences
- Bayesian
- Counterfactual distributions
- Potential outcomes
- Treatment effects