TY - JOUR
T1 - A Falsifiability Characterization of Double Robustness Through Logical Operators
AU - Frangakis, Constantine
N1 - Publisher Copyright:
© 2019 Walter de Gruyter GmbH, Berlin/Boston.
PY - 2019/3/1
Y1 - 2019/3/1
N2 - We address the characterization of problems in which a consistent estimator exists in a union of two models, also termed as a doubly robust estimator. Such estimators are important in missing information, including causal inference problems. Existing characterizations, based on the semiparametric theory of projections, have seen sufficient progress, but can still leave one's understanding less than satisfied as to when and especially why such estimation works. We explore here a different, explanatory characterization - an exegesis based on logical operators. We show that double robustness exists if and only if we can produce consistent estimators for each contributing model based on an "AND" estimator, i. e., an estimator whose consistency generally needs both models to be correct. We show how this characterization explains double robustness through falsifiability.
AB - We address the characterization of problems in which a consistent estimator exists in a union of two models, also termed as a doubly robust estimator. Such estimators are important in missing information, including causal inference problems. Existing characterizations, based on the semiparametric theory of projections, have seen sufficient progress, but can still leave one's understanding less than satisfied as to when and especially why such estimation works. We explore here a different, explanatory characterization - an exegesis based on logical operators. We show that double robustness exists if and only if we can produce consistent estimators for each contributing model based on an "AND" estimator, i. e., an estimator whose consistency generally needs both models to be correct. We show how this characterization explains double robustness through falsifiability.
KW - double robustness
KW - logical operators
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U2 - 10.1515/jci-2018-0016
DO - 10.1515/jci-2018-0016
M3 - Article
AN - SCOPUS:85063098149
SN - 2193-3677
VL - 7
JO - Journal of Causal Inference
JF - Journal of Causal Inference
IS - 1
M1 - 20180016
ER -