The Elephant in the Corner: A Cautionary Tale About Measurement Error in Treatment Effects Models
By Daniel L. Millimet. Social Science Research Network, 2010
50 citations recorded by the source corpus.
Researchers in economics and other disciplines are often interested in the causal effect of a binary treatment on outcomes. Econometric methods used to estimate such effects are divided into one of two strands depending on whether they require unconfoundedness (i.e., independence of potential outcomes and treatment assignment conditional on a set of observable covariates). When this assumption holds, researchers now have a wide array of estimation techniques from which to choose. However, very little is known about their performance – both in absolute and relative terms – when measurement error is present. In this study, the performance of several estimators that require unconfoundedness, as well as some that do not, are evaluated in a Monte Carlo study. In all cases, the data-generating process is such that unconfoundedness holds with the ‘real’ data. However, measurement error is then introduced. Specifically, three types of measurement error are considered: (i) errors in treatment assignment, (ii) errors in the outcome, and (iii) errors in the vector of covariates. Recommendations for researchers are provided.
Subfields: Economics and Econometrics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/4e850cfce0066cf95ebef42be4478bf6ac3a1583
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
In Statistics and Probability
Citations in the corpus
- On the estimation of treatment effects with endogenous misreporting (2016, background): “Similarly, Millimet (2011) studies the performance of several estimators employed in the causal inference literature while introducing measurement error in the treatment (binary) regressor, outcome variable, and vector of covariates, and cautions researchers to be conscious of the consequences of…”