Abstract This article studies the treatment effect models in which individuals are classified into unobserved groups based on heterogeneous rules. By using a finite mixture approach, we propose marginal (MTE) framework choice and outcome equations can be across groups. Under availability of instrumental variables specific to each group, show that MTE for group separately identified. On basis ou...

This paper develops analytic results for marginal compensated effects in discrete labor supply models, including a Slutsky equation. The equation is aggregate the sense that it establishes relationship between of probability working and mean hours work terms corresponding uncompensated effects. differs somewhat from standard continuous models. Specifically, effect an increase wage rate decrease...

The purpose in diagnostic reasoning is to find the cause of observed effects by applying knowledge about the effects and their potential causes. In the causal structure linking causes and effects, effects can share causes or be linked more indirectly. The causal diversity effect reflects the increased support of a cause by a more widespread distribution of effects within the underlying causal s...

The key quantity needed for Bayesian hypothesis testing and model selection is the marginal likelihood for a model, also known as the integrated likelihood, or the marginal probability of the data. In this paper we describe a way to use posterior simulation output to estimate marginal likelihoods. vVe describe the basic LaplaceMetropolis estimator for models without random effects. For models w...