1,721,060 research outputs found
Comparing principal stratification and selection models in parametric causal inference with nonignorable missingness
No full textTwo 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
Using secondary outcomes and covariates to sharpen inference in randomized experiments with noncompliance. Working paper 2012/04 Dipartimento di Statistica Università di Firenze
No full textUsing secondary outcomes to sharpen inference in randomized experiments with noncompliance
No full textExploiting instrumental variables in causal Inference with nonignorable outcome nonresponse using principal stratification. Presentato a 63th Econometric Society European Meeting, Milano.
No full textFinding alternative sources of identification in generalized selection models using principal stratification. Presentato a International Conference on Causal Inference, Uppsala.
No full textFinding alternative sources of identification in generalized selection models using principal stratification. Working Paper 2007/09 Dipartimento di Statistica Università di Firenze
No full textExploiting instrumental variables in causal Inference with nonignorable outcome nonresponse using principal stratification
No full textWhen the goal of inference is estimating causal effects, we usually have to face problems
related to the fact that treatment assignment is not under the control of the investigator;
in addition some studies may be affected by different sorts of post-treatment selection of
observations due to, e.g., non response, truncation or censoring “due to death”. All such
complications require to somehow control for them, but the use of the standard statistical
conditioning may be in general improper (Rubin, 1974; Rosembaum, 1984).
In this paper we consider a specific post-treatment complication, namely the problem
of nonignorable nonresponse on an outcome variable in observational studies. This is
a typical topic usually known in the econometric literature as endogenous selection;
here we tackle this problem specifically within a causal inference framework. By
exploiting Principal Stratification (Frangakis and Rubin, 2002), we analyze and propose
identification strategies in the presence of an instrumental variable for nonresponse. We
focus on the different role and meaning of the instrumental variable, also by comparing
our framework with a general nonseparable selection model setting. As a motivating
example we consider a simplified evaluation study in the field of financial aids to
firms, where typically missingness on the outcome variables, such as variables related
to firms’performances, can rarely be assumed missing at random
Using Secondary Outcomes and Covariates to Sharpen Inference in Instrumental Variable. Presentato a 7th IZA Conference on Labor Market Policy Evaluation. Harvard University,Cambridge MA, USA Settings
No full textEstimating linear models with ordinal qualitative regressors by maximum likelihood. A comparison among estimation methods
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