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Conditional Front-door Adjustment for Heterogeneous Treatment Assignment Effect Estimation Under Non-adherence

arXiv 2505.05677 ↗First posted 8 May 2025cs.LGcited by 0

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Abstract (arXiv)

Estimates of heterogeneous treatment assignment effects can inform treatment decisions. Under the presence of non-adherence (e.g., patients do not adhere to their assigned treatment), both the standard backdoor adjustment (SBD) and the conditional front-door adjustment (CFD) can recover unbiased estimates of the treatment assignment effects. However, the estimation variance of these approaches may vary widely across settings, which remains underexplored in the literature. In this work, we demonstrate theoretically and empirically that CFD yields lower-variance estimates than SBD when the true effect of treatment assignment is small (i.e., assigning an intervention leads to small changes in patients' future outcome). Additionally, since CFD requires estimating multiple nuisance parameters, we introduce LobsterNet, a multi-task neural network that implements CFD with joint modeling of the nuisance parameters. Empirically, LobsterNet reduces estimation error across several semi-synthetic and real-world datasets compared to baselines. Our findings suggest CFD with shared nuisance parameter modeling can improve treatment assignment effect estimation under non-adherence.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 37 references that are in the map. First posted on arXiv 8 May 2025.

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References · 10

  1. 2017Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learningcited by 478
  2. 2019Adapting Neural Networks for the Estimation of Treatment Effectscited by 287
  3. 2021On Inductive Biases for Heterogeneous Treatment Effect Estimationcited by 75
  4. 2016Double/Debiased Machine Learning for Treatment and Causal Parameterscited by 43
  5. 2021Causal Effect Inference for Structured Treatmentscited by 39
  6. 2017Estimation of causal effects with multiple treatments: a review and new ideascited by 29
  7. 2023Causal Inference with Conditional Front-Door Adjustment and Identifiable Variational Autoencodercited by 17
  8. 2021End-to-End Balancing for Causal Continuous Treatment-Effect Estimationcited by 9
  9. 2020Estimating Treatment Effects with Observed Confounders and Mediatorscited by 8
  10. 2019Heterogeneous causal effects with imperfect compliance: a Bayesian machine learning approachcited by 6

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