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Comparing principal stratification and selection models in parametric causal inference with nonignorable missingness

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  • Mealli, Fabrizia
  • Pacini, Barbara

Abstract

Two approaches for dealing with "endogenous selection" problems when estimating causal effects are considered. They are principal stratification and selection models. The main goal is to highlight similarities and differences between the two approaches, by investigating the different nature of their parametric hypotheses. The principal stratification approach focuses on information contained in specific subgroups of units. The aim is to produce valid inference conditional on such subgroups, without an a priori extension of the results to the whole population. Selection models, on the contrary, aim at estimating parameters that should be valid for the whole population, as if the data come from random sampling. A simulation study is conducted to show their different performances, with data generating processes coming from either approach. It is also argued that principal stratification is able to suggest alternative identification strategies not always easily translatable into assumptions of a selection model.

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Bibliographic Info

Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

Volume (Year): 53 (2008)
Issue (Month): 2 (December)
Pages: 507-516

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Handle: RePEc:eee:csdana:v:53:y:2008:i:2:p:507-516

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Cited by:
  1. Giovanni Mellace & Roberto Rocci, 2011. "Principal Stratification in sample selection problems with non normal error terms," CEIS Research Paper 194, Tor Vergata University, CEIS, revised 02 May 2011.
  2. Huber, Martin & Mellace, Giovanni, 2011. "Sharp bounds on causal effects under sample selection," Economics Working Paper Series 1134, University of St. Gallen, School of Economics and Political Science.

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