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Coupling Generative Modeling and an Autoencoder with the Causal Bridge

arXiv 2509.25599 ↗First posted 29 Sep 2025stat.ML cs.LG stat.MEcited by 0

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Abstract (arXiv)

We consider inferring the causal effect of a treatment (intervention) on an outcome of interest in situations where there is potentially an unobserved confounder influencing both the treatment and the outcome. This is achievable by assuming access to two separate sets of control (proxy) measurements associated with treatment and outcomes, which are used to estimate treatment effects through a function termed the em causal bridge (CB). We present a new theoretical perspective, associated assumptions for when estimating treatment effects with the CB is feasible, and a bound on the average error of the treatment effect when the CB assumptions are violated. From this new perspective, we then demonstrate how coupling the CB with an autoencoder architecture allows for the sharing of statistical strength between observed quantities (proxies, treatment, and outcomes), thus improving the quality of the CB estimates. Experiments on synthetic and real-world data demonstrate the effectiveness of the proposed approach in relation to the state-of-the-art methodology for proxy measurements.

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References · 20

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2014Conditional Generative Adversarial Netscited by 2,162
  3. 2016InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Netscited by 860
  4. 2016f-GAN: Training Generative Neural Samplers using Variational Divergence Minimizationcited by 506
  5. 2017Causal Effect Inference with Deep Latent-Variable Modelscited by 410
  6. 2017Information-theoretic analysis of generalization capability of learning algorithmscited by 303
  7. 2016Identifying Causal Effects With Proxy Variables of an Unmeasured Confoundercited by 139
  8. 2020Learning Deep Features in Instrumental Variable Regressioncited by 66
  9. 2016Balancing Covariates via Propensity Score Weightingcited by 65
  10. 2020Semiparametric proximal causal inferencecited by 59
  11. 2018Interval Estimation of Individual-Level Causal Effects Under Unobserved Confoundingcited by 51
  12. 2020Counterfactual Representation Learning with Balancing Weightscited by 45
8 more
  1. 2021Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluationcited by 40
  2. 2018A confounding bridge approach for double negative control inference on causal effectscited by 36
  3. 2021A Critical Look at the Consistency of Causal Estimation With Deep Latent Variable Modelscited by 29
  4. 2018Flexible sensitivity analysis for observational studies without observable implicationscited by 18
  5. 2022Deep Learning Methods for Proximal Inference via Maximum Moment Restrictioncited by 14
  6. 2022Proximal Causal Inference for Marginal Counterfactual Survival Curvescited by 5
  7. 2025Density Ratio-based Proxy Causal Learning Without Density Ratioscited by 3
  8. 2024Causal Effect Estimation using identifiable Variational AutoEncoder with Latent Confounders and Post-Treatment Variablescited by 1

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