Conditional front-door adjustment can beat standard backdoor adjustment for treatment-assignment effect estimation under non-adherence, especially when effects are small, and a shared-representation network improves its accuracy.
The paper of how: Estimating treatment effects using the front-door criterion
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Conditional Front-door Adjustment for Heterogeneous Treatment Assignment Effect Estimation Under Non-adherence
Conditional front-door adjustment can beat standard backdoor adjustment for treatment-assignment effect estimation under non-adherence, especially when effects are small, and a shared-representation network improves its accuracy.