Open AccessA note on adapting propensity score matching and selection models to choice based samples
Author(s) -
Heckman James J.,
Todd Petra E.
Publication year - 2009
Publication title -
the econometrics journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.861
H-Index - 36
eISSN - 1368-423X
pISSN - 1368-4221
DOI - 10.1111/j.1368-423x.2008.00269.x
Subject(s) - propensity score matching , selection (genetic algorithm) , matching (statistics) , statistics , odds , sampling (signal processing) , selection bias , econometrics , mathematics , computer science , artificial intelligence , logistic regression , filter (signal processing) , computer vision
Summary The probability of selection into treatment plays an important role in matching and selection models. However, this probability can often not be consistently estimated, because of choice‐based sampling designs with unknown sampling weights. This note establishes that the selection and matching procedures can be implemented using propensity scores fit on choice‐based samples with misspecified weights, because the odds ratio of the propensity score fit on the choice‐based sample is monotonically related to the odds ratio of the true propensity scores.