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Doubly Robust Estimation of Causal Effects in Network-Based Observational Studies | | Vanessa McNealis
; Erica E. M. Moodie
; Nema Dean
; | | Date: |
Wed, 1 Feb 2023 04:20:26 GMT (1879kb) | | Abstract: | Causal inference on populations embedded in social networks poses technical
challenges, since the typical no interference assumption may no longer hold.
For instance, in the context of social research, the outcome of a study unit
will likely be affected by an intervention or treatment received by close
neighbors. While inverse probability-of-treatment weighted (IPW) estimators
have been developed for this setting, they are often highly inefficient. In
this work, we assume that the network is a union of disjoint components and
propose doubly robust (DR) estimators combining models for treatment and
outcome that are consistent and asymptotically normal if either model is
correctly specified. We present empirical results that illustrate the DR
property and the efficiency gain of DR over IPW estimators when both the
outcome and treatment models are correctly specified. Simulations are conducted
for networks with equal and unequal component sizes and outcome data with and
without a multilevel structure. We apply these methods in an illustrative
analysis using the Add Health network, examining the impact of maternal college
education on adolescent school performance, both direct and indirect. | | Source: | arXiv, 2302.00230 | | Services: | Forum | Review | PDF | Favorites | |
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