Disentangled Double Machine Learning for Accurate Causal Effect Estimation

• URL: http://arxiv.org/abs/2605.24808v1
• Date: Sun, 24 May 2026 01:40:43 GMT
• Title: Disentangled Double Machine Learning for Accurate Causal Effect Estimation
• Abstract summary: Confounding bias is a key challenge in causal effect estimation from observational data.<n>Double Machine Learning (DML) addresses this issue by estimating treatment and outcome nuisance functions.<n>DML often produces biased and unstable estimates in highdimensional or finite-sample scenarios.<n>We propose Disentangled Double Machine Learning (DDML), a novel algorithm that integrates two key strategies.
• Score: 80.07136847623498
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Confounding bias is a key challenge in causal effect estimation from observational data. Double Machine Learning (DML) addresses this issue by estimating treatment and outcome nuisance functions, constructing treatment and outcome residuals, and estimating causal effects from the residuals. However, DML often produces biased and unstable estimates in highdimensional or finite-sample scenarios. One reason is that DML estimates nuisance functions using all covariates without disentangling distinct latent factors, resulting in unreliable nuisance function estimation. Another is that imprecise nuisance estimation further introduces residual dependence between the treatment residual and the remaining outcome error, undermining the accuracy of causal effect estimates. To address these issues, in this paper, we propose Disentangled Double Machine Learning (DDML), a novel algorithm that integrates two key strategies. First, a causal role disentanglement strategy decomposes covariates into confounders, treatment-specific factors, and outcomespecific factors for enabling reliable nuisance function estimation. And second, a residual dependence orthogonalization strategy mitigates residual dependence caused by nuisance estimation errors for enhancing the precision of causal effect estimates. Experimental results on synthetic, semi-synthetic, and real-world datasets demonstrate that DDML significantly outperforms 13 state-of-the-art baseline algorithms in both MAE and RMSE.

Related papers

• Learning heterogeneous treatment effects under principal stratification [4.965155763091777]
  We study the identification and estimation of the conditional principal causal effects under principal ignorability.<n>We propose a novel doubly cross-fit doubly robust machine that resolves the nested nuisance structure inherent to principal stratification.
  arXiv  Detail & Related papers  (2026-06-27T20:27:17Z)
• Estimating Treatment Effects with Independent Component Analysis [30.83679633039883]
  We show that Independent Component Analysis (ICA) can be used for causal effect estimation in the partially linear regression (PLR) model.<n>We find that linear ICA can accurately estimate multiple treatment effects even in the presence of Gaussian confounders or nonlinear nuisance.
  arXiv  Detail & Related papers  (2025-07-22T11:16:23Z)
• A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation [55.53426007439564]
  Estimating individualized treatment effects from observational data is a central challenge in causal inference.<n>In inverse probability weighting (IPW) is a well-established solution to this problem, but its integration into modern deep learning frameworks remains limited.<n>We propose Importance-Weighted Diffusion Distillation (IWDD), a novel generative framework that combines the pretraining of diffusion models with importance-weighted score distillation.
  arXiv  Detail & Related papers  (2025-05-16T17:00:52Z)
• Black Box Causal Inference: Effect Estimation via Meta Prediction [56.277798874118425]
  We frame causal inference as a dataset-level prediction problem, offloading algorithm design to the learning process.<n>We introduce, called black box causal inference (BBCI), builds estimators in a black-box manner by learning to predict causal effects from sampled dataset-effect pairs.<n>We demonstrate accurate estimation of average treatment effects (ATEs) and conditional average treatment effects (CATEs) with BBCI across several causal inference problems.
  arXiv  Detail & Related papers  (2025-03-07T23:43:19Z)
• Doubly Robust Proximal Causal Learning for Continuous Treatments [56.05592840537398]
  We propose a kernel-based doubly robust causal learning estimator for continuous treatments. We show that its oracle form is a consistent approximation of the influence function. We then provide a comprehensive convergence analysis in terms of the mean square error.
  arXiv  Detail & Related papers  (2023-09-22T12:18:53Z)
• Proximal Causal Learning of Conditional Average Treatment Effects [0.0]
  We propose a tailored two-stage loss function for learning heterogeneous treatment effects. Our proposed estimator can be implemented by off-the-shelf loss-minimizing machine learning methods.
  arXiv  Detail & Related papers  (2023-01-26T02:56:36Z)
• Discovering Heterogeneous Treatment Effects in Regression Discontinuity Designs [0.0]
  The paper proposes a causal supervised machine learning algorithm to uncover treatment effect heterogeneity in fuzzy regression discontinuity designs.<n>We study the performance of the method through Monte Carlo simulations and apply it to uncover various sources of heterogeneity in the impact of attending a better secondary school in Romania.
  arXiv  Detail & Related papers  (2021-06-22T09:47:28Z)
• Efficient Causal Inference from Combined Observational and Interventional Data through Causal Reductions [68.6505592770171]
  Unobserved confounding is one of the main challenges when estimating causal effects. We propose a novel causal reduction method that replaces an arbitrary number of possibly high-dimensional latent confounders. We propose a learning algorithm to estimate the parameterized reduced model jointly from observational and interventional data.
  arXiv  Detail & Related papers  (2021-03-08T14:29:07Z)
• Confounding Feature Acquisition for Causal Effect Estimation [6.174721516017138]
  We frame this challenge as a problem of feature acquisition of confounding features for causal inference. Our goal is to prioritize acquiring values for a fixed and known subset of missing confounders in samples that lead to efficient average treatment effect estimation.
  arXiv  Detail & Related papers  (2020-11-17T16:28:43Z)
• Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and Beyond [69.83813153444115]
  We consider an efficient estimating equation for the (local) quantile treatment effect ((L)QTE) in causal inference. Debiased machine learning (DML) is a data-splitting approach to estimating high-dimensional nuisances. We propose localized debiased machine learning (LDML), which avoids this burdensome step.
  arXiv  Detail & Related papers  (2019-12-30T14:42:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.