mlsanityData to 7 Oct 2026

Efficient adjustment for complex covariates: Gaining efficiency with DOPE

arXiv 2402.12980 ↗First posted 20 Feb 2024math.ST stat.ME stat.MLcited by 5

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Abstract (arXiv)

Covariate adjustment is a ubiquitous method used to estimate the average treatment effect (ATE) from observational data. Assuming a known graphical structure of the data generating model, recent results give graphical criteria for optimal adjustment, which enables efficient estimation of the ATE. However, graphical approaches are challenging for high-dimensional and complex data, and it is not straightforward to specify a meaningful graphical model of non-Euclidean data such as texts. We propose a new framework that accommodates adjustment for any subset of information expressed by the covariates, and we show that the information that is minimally sufficient for prediction of the outcome given the treatment is also most efficient for adjustment. Based on our theoretical results, we propose the Debiased Outcome-adapted Propensity Estimator (DOPE) for efficient estimation of the ATE, and we provide asymptotic results for DOPE under general conditions. Compared to the augmented inverse propensity weighted (AIPW) estimator, DOPE can retain its efficiency even when the covariates are highly predictive of treatment. We illustrate this with a single-index model, and with an implementation of DOPE based on neural networks, we demonstrate its performance on simulated and real data. Our results show that DOPE provides an efficient and robust methodology for ATE estimation in various observational settings.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 13 of its 48 references that are in the map. First posted on arXiv 20 Feb 2024.

Builds on

13 of 48 references.

References · 13

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1 more
  1. 2023Perturbation-based Effect Measures for Compositional Datacited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 2.

5 papers.

  1. 2025What is causal about causal models and representations?cited by 6
  2. 2025Orthogonal Representation Learning for Estimating Causal Quantitiescited by 4
  3. 2025Adjustment for Confounding using Pre-Trained Representationscited by 3
  4. 2026Unveiling the Structure of Do-Calculus Reasoning via Derivation Graphscited by 1
  5. 2026Outcome-adapted Automatic Debiased Machine Learningcited by 0