mlsanityData to 14 Oct 2026

DiffPO: A causal diffusion model for learning distributions of potential outcomes

arXiv 2410.08924 ↗First posted 11 Oct 2024cs.LGcited by 17

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

Predicting potential outcomes of interventions from observational data is crucial for decision-making in medicine, but the task is challenging due to the fundamental problem of causal inference. Existing methods are largely limited to point estimates of potential outcomes with no uncertain quantification; thus, the full information about the distributions of potential outcomes is typically ignored. In this paper, we propose a novel causal diffusion model called DiffPO, which is carefully designed for reliable inferences in medicine by learning the distribution of potential outcomes. In our DiffPO, we leverage a tailored conditional denoising diffusion model to learn complex distributions, where we address the selection bias through a novel orthogonal diffusion loss. Another strength of our DiffPO method is that it is highly flexible (e.g., it can also be used to estimate different causal quantities such as CATE). Across a wide range of experiments, we show that our method achieves state-of-the-art performance.

Key facts. Cited by 17 papers in the map (data through 14 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 42 of its 67 references that are in the map. First posted on arXiv 11 Oct 2024.

Builds on

42 of 67 references.

Closest ancestors · 10

Same direction, newest first

  1. 2024Causal Diffusion Autoencoders: Toward Counterfactual Generation via Diffusion Probabilistic Models27 Apr
  2. 2023Diffusion Models are Minimax Optimal Distribution Estimators3 Mar
  3. 2023Consistency Models2 Mar
  4. 2022Diffusion Models for Causal Discovery via Topological Ordering12 Oct
  5. 2022GENIE: Higher-Order Denoising Diffusion Solvers11 Oct
  6. 2022DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps2 Jun
  7. 2022Fast Sampling of Diffusion Models with Exponential Integrator29 Apr
  8. 2021Diffusion Models Beat GANs on Image Synthesis11 May
  9. 2020Denoising Diffusion Implicit Models6 Oct
  10. 2020Denoising Diffusion Probabilistic Models19 Jun

Other references · 32

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  3. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,101
  4. 2015Estimation and Inference of Heterogeneous Treatment Effects using Random Forestscited by 600
  5. 2021CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputationcited by 582
  6. 2017Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learningcited by 478
  7. 2017Quasi-Oracle Estimation of Heterogeneous Treatment Effectscited by 332
  8. 2016Learning Representations for Counterfactual Inferencecited by 329
  9. 2017Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processescited by 158
  10. 2020Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representationscited by 155
  11. 2019Orthogonal Statistical Learningcited by 151
  12. 2021Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithmscited by 144
20 more
  1. 2020Causal Autoregressive Flowscited by 99
  2. 2022Diffusion Causal Models for Counterfactual Estimationcited by 87
  3. 2020Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networkscited by 78
  4. 2017Double/Debiased/Neyman Machine Learning of Treatment Effectscited by 76
  5. 2021On Inductive Biases for Heterogeneous Treatment Effect Estimationcited by 75
  6. 2020Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effectscited by 72
  7. 2020Treatment effect estimation with disentangled latent factorscited by 63
  8. 2020Counterfactual Representation Learning with Balancing Weightscited by 45
  9. 2018Learning Weighted Representations for Generalization Across Designscited by 41
  10. 2021Is MC Dropout Bayesian?cited by 35
  11. 2023In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimationcited by 29
  12. 2023Bayesian Neural Controlled Differential Equations for Treatment Effect Estimationcited by 26
  13. 2021Estimating Average Treatment Effects via Orthogonal Regularizationcited by 25
  14. 2022Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimationcited by 25
  15. 2023Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimationcited by 20
  16. 2021VACA: Design of Variational Graph Autoencoders for Interventional and Counterfactual Queriescited by 19
  17. 2017Bayesian Nonparametric Causal Inference: Information Rates and Learning Algorithmscited by 16
  18. 2017Towards Understanding the Invertibility of Convolutional Neural Networkscited by 10
  19. 2023Orthogonal prediction of counterfactual outcomescited by 7
  20. 2024Causal Machine Learning for Cost-Effective Allocation of Development Aidcited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 12; 2026: 5.

17 papers.

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  1. 2025CausalPFN: Amortized Causal Effect Estimation via In-Context Learningcited by 49
  2. 2025Foundation Models for Causal Inference via Prior-Data Fitted Networkscited by 32
  3. 2026LightningRL: Breaking the Accuracy-Parallelism Trade-off of Block-wise dLLMs via Reinforcement Learningcited by 6
  4. 2025Counterfactual Probabilistic Diffusion with Expert Modelscited by 4
  5. 2025Improving the Generation and Evaluation of Synthetic Data for Downstream Medical Causal Inferencecited by 4
  6. 2025Flow-based Generative Modeling of Potential Outcomes and Counterfactualscited by 3
  7. 2025A Diffusion-Based Method for Learning the Multi-Outcome Distribution of Medical Treatmentscited by 3
  8. 2025CausalDiffTab: Mixed-Type Causal-Aware Diffusion for Tabular Data Generationcited by 3
  9. 2025PrivATE: Differentially Private Confidence Intervals for Average Treatment Effectscited by 2
  10. 2025LLM-Driven Treatment Effect Estimation Under Inference Time Text Confoundingcited by 2
  11. 2025SurvDiff: A Diffusion Model for Generating Synthetic Data in Survival Analysiscited by 1
  12. 2025GDR-learners: Orthogonal Learning of Generative Models for Potential Outcomescited by 1
5 more
  1. 2026Retrospective Counterfactual Prediction by Conditioning on the Factual Outcome: A Cross-World Approachcited by 1
  2. 2025A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillationcited by 0
  3. 2026Interpreting the Synchronization Gap: The Hidden Mechanism Inside Diffusion Transformerscited by 0
  4. 2026RepFlow: Representation Enhanced Flow Matching for Causal Effect Estimationcited by 0
  5. 2026Clinically-Grounded Counterfactual Reasoning for Medical Video Diagnosiscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Causal Diffusion Models for Counterfactual Outcome Distributions in Longitudinal Data
  2. 2023Diffusion Model in Causal Inference with Unmeasured Confounders
  3. 2025Diffusion Models in Simulation-Based Inference: A Tutorial Review
  4. 2025When Additive Noise Meets Unobserved Mediators: Bivariate Denoising Diffusion for Causal Discovery
  5. 2026Smoothing the Landscape: Causal Structure Learning via Diffusion Denoising Objectives
  6. 2024DiffusionCounterfactuals: Inferring High-dimensional Counterfactuals with Guidance of Causal Representations
  7. 2026Causality-Encoded Diffusion Models for Interventional Sampling and Edge Inference
  8. 2024DiffPuter: Empowering Diffusion Models for Missing Data Imputation