Log In Sign Up

Identification and estimation of nonignorable missing outcome mean without identifying the full data distribution

by   Wei Li, et al.

We consider the problem of making inference about the population outcome mean of an outcome variable subject to nonignorable missingness. By leveraging a so-called shadow variable for the outcome, we propose a novel condition that ensures nonparametric identification of the outcome mean, although the full data distribution is not identified. The identifying condition requires the existence of a function as a solution to a representer equation that connects the shadow variable to the outcome mean. Under this condition, we use sieves to nonparametrically solve the representer equation and propose an estimator which avoids modeling the propensity score or the outcome regression. We establish the asymptotic properties of the proposed estimator. We also show that the estimator is locally efficient and attains the semiparametric efficiency bound for the shadow variable model under certain regularity conditions. We illustrate the proposed approach via simulations and a real data application on home pricing.


page 1

page 2

page 3

page 4


Instrumental variable estimation of dynamic treatment effects on a survival outcome

This paper considers identification and estimation of the causal effect ...

Nonclassical Measurement Error in the Outcome Variable

We study a semi-/nonparametric regression model with a general form of n...

A Versatile Estimation Procedure without Estimating the Nonignorable Missingness Mechanism

We consider the estimation problem in a regression setting where the out...

Semiparametric Inference for Non-monotone Missing-Not-at-Random Data: the No Self-Censoring Model

We study the identification and estimation of statistical functionals of...

Counterfactual inference for sequential experimental design

We consider the problem of counterfactual inference in sequentially desi...

Fused mean structure learning in data integration with dependence

Motivated by image-on-scalar regression with data aggregated across mult...