Abstract:
:The positivity assumption, or the experimental treatment assignment (ETA) assumption, is important for identifiability in causal inference. Even if the positivity assumption holds, practical violations of this assumption may jeopardize the finite sample performance of the causal estimator. One of the consequences of practical violations of the positivity assumption is extreme values in the estimated propensity score (PS). A common practice to address this issue is truncating the PS estimate when constructing PS-based estimators. In this study, we propose a novel adaptive truncation method, Positivity-C-TMLE, based on the collaborative targeted maximum likelihood estimation (C-TMLE) methodology. We demonstrate the outstanding performance of our novel approach in a variety of simulations by comparing it with other commonly studied estimators. Results show that by adaptively truncating the estimated PS with a more targeted objective function, the Positivity-C-TMLE estimator achieves the best performance for both point estimation and confidence interval coverage among all estimators considered.
journal_name
Stat Methods Med Resjournal_title
Statistical methods in medical researchauthors
Ju C,Schwab J,van der Laan MJdoi
10.1177/0962280218774817subject
Has Abstractpub_date
2019-06-01 00:00:00pages
1741-1760issue
6eissn
0962-2802issn
1477-0334journal_volume
28pub_type
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