mlsanityData to 14 Oct 2026

Deep End-to-end Causal Inference

arXiv 2202.02195 ↗First posted 4 Feb 2022stat.ML cs.LGcited by 95

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Abstract (arXiv)

Causal inference is essential for data-driven decision making across domains such as business engagement, medical treatment and policy making. However, research on causal discovery has evolved separately from inference methods, preventing straight-forward combination of methods from both fields. In this work, we develop Deep End-to-end Causal Inference (DECI), a single flow-based non-linear additive noise model that takes in observational data and can perform both causal discovery and inference, including conditional average treatment effect (CATE) estimation. We provide a theoretical guarantee that DECI can recover the ground truth causal graph under standard causal discovery assumptions. Motivated by application impact, we extend this model to heterogeneous, mixed-type data with missing values, allowing for both continuous and discrete treatment decisions. Our results show the competitive performance of DECI when compared to relevant baselines for both causal discovery and (C)ATE estimation in over a thousand experiments on both synthetic datasets and causal machine learning benchmarks across data-types and levels of missingness.

Key facts. Cited by 95 papers in the map (data through 14 Oct 2026). Builds on 46 of its 109 references that are in the map. First posted on arXiv 4 Feb 2022.

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46 of 109 references.

References · 46

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2016Categorical Reparameterization with Gumbel-Softmaxcited by 2,739
  3. 2015Variational Inference with Normalizing Flowscited by 2,170
  4. 2016Variational Inference: A Review for Statisticianscited by 1,472
  5. 2016The Concrete Distribution: A Continuous Relaxation of Discrete Random Variablescited by 1,148
  6. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 752
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  8. 2015Estimation and Inference of Heterogeneous Treatment Effects using Random Forestscited by 600
  9. 2019Neural Spline Flowscited by 596
  10. 2017Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learningcited by 478
  11. 2017Causal Effect Inference with Deep Latent-Variable Modelscited by 410
  12. 2013Causal Discovery with Continuous Additive Noise Modelscited by 255
34 more
  1. 2017Advances in Variational Inferencecited by 236
  2. 2019Gradient-Based Neural DAG Learningcited by 227
  3. 2020Deep Structural Causal Models for Tractable Counterfactual Inferencecited by 211
  4. 2018Neural Autoregressive Flowscited by 208
  5. 2019Learning Sparse Nonparametric DAGscited by 207
  6. 2020On the Role of Sparsity and DAG Constraints for Learning Linear DAGscited by 191
  7. 2013CAM: Causal additive models, high-dimensional order search and penalized regressioncited by 178
  8. 2020Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representationscited by 155
  9. 2021DiBS: Differentiable Bayesian Structure Learningcited by 106
  10. 2020Causal Autoregressive Flowscited by 99
  11. 2016Orthogonal Random Featurescited by 91
  12. 2013High-dimensional learning of linear causal networks via inverse covariance estimationcited by 87
  13. 2018EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAEcited by 87
  14. 2021Efficient Neural Causal Discovery without Acyclicity Constraintscited by 82
  15. 2021BCD Nets: Scalable Variational Approaches for Bayesian Causal Discoverycited by 68
  16. 2016Balancing Covariates via Propensity Score Weightingcited by 65
  17. 2021gCastle: A Python Toolbox for Causal Discoverycited by 61
  18. 2017Frequentist Consistency of Variational Bayescited by 60
  19. 2020Differentiable Causal Discovery Under Unmeasured Confoundingcited by 56
  20. 2019A Graph Autoencoder Approach to Causal Structure Learningcited by 55
  21. 2018Causal Discovery in the Presence of Missing Datacited by 47
  22. 2021Unsuitability of NOTEARS for Causal Graph Discoverycited by 47
  23. 2021Variational Causal Networks: Approximate Bayesian Inference over Causal Structurescited by 43
  24. 2020Towards Scalable Bayesian Learning of Causal DAGscited by 33
  25. 2013Directed Cyclic Graphical Representations of Feedback Modelscited by 28
  26. 2017Fast Causal Inference with Non-Random Missingness by Test-Wise Deletioncited by 23
  27. 2019Variational Bayes under Model Misspecificationcited by 22
  28. 2021DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptionscited by 20
  29. 2015Selective Greedy Equivalence Search: Finding Optimal Bayesian Networks Using a Polynomial Number of Score Evaluationscited by 14
  30. 2020Minimal enumeration of all possible total effects in a Markov equivalence classcited by 12
  31. 2021Estimating the Long-Term Effects of Novel Treatmentscited by 8
  32. 2019Frequentist Consistency of Generalized Variational Inferencecited by 6
  33. 2019Diagnosis of Autism Spectrum Disorder by Causal Influence Strength Learned from Resting-State fMRI Datacited by 4
  34. 2021Active Slices for Sliced Stein Discrepancycited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 10; 2023: 32; 2024: 23; 2025: 11; 2026: 19.

95 papers.

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  1. 2022Causal Machine Learning for Healthcare and Precision Medicinecited by 68
  2. 2022Diffusion Models for Causal Discovery via Topological Orderingcited by 66
  3. 2023Understanding Causality with Large Language Models: Feasibility and Opportunitiescited by 53
  4. 2023High Fidelity Image Counterfactuals with Probabilistic Causal Modelscited by 53
  5. 2023Causal normalizing flows: from theory to practicecited by 51
  6. 2023Counterfactual Identifiability of Bijective Causal Modelscited by 43
  7. 2023CUTS+: High-dimensional Causal Discovery from Irregular Time-seriescited by 41
  8. 2023CausalTime: Realistically Generated Time-series for Benchmarking of Causal Discoverycited by 36
  9. 2023CUTS: Neural Causal Discovery from Irregular Time-Series Datacited by 33
  10. 2023BayesDAG: Gradient-Based Posterior Inference for Causal Discoverycited by 33
  11. 2024The Essential Role of Causality in Foundation World Models for Embodied AIcited by 32
  12. 2022Rhino: Deep Causal Temporal Relationship Learning With History-dependent Noisecited by 30
38 more
  1. 2022MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Modelscited by 22
  2. 2024Sample, estimate, aggregate: A recipe for causal discovery foundation modelscited by 21
  3. 2023Counterfactual (Non-)identifiability of Learned Structural Causal Modelscited by 17
  4. 2023Towards Causal Foundation Model: on Duality between Causal Inference and Attentioncited by 17
  5. 2023Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learningcited by 15
  6. 2023Causal Semantic Communication for Digital Twins: A Generalizable Imitation Learning Approachcited by 14
  7. 2023Neural Structure Learning with Stochastic Differential Equationscited by 14
  8. 2025Black Box Causal Inference: Effect Estimation via Meta Predictioncited by 13
  9. 2025Estimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learningcited by 13
  10. 2024Mitigating attribute amplification in counterfactual image generationcited by 12
  11. 2022Deep Causal Learning: Representation, Discovery and Inferencecited by 11
  12. 2023Local Causal Discovery for Estimating Causal Effectscited by 10
  13. 2023Discovering Mixtures of Structural Causal Models from Time Series Datacited by 10
  14. 2022On the Generalization and Adaption Performance of Causal Modelscited by 8
  15. 2024Causality for Tabular Data Synthesis: A High-Order Structure Causal Benchmark Frameworkcited by 8
  16. 2023Cause-Effect Inference in Location-Scale Noise Models: Maximum Likelihood vs. Independence Testingcited by 7
  17. 2024A Fixed-Point Approach for Causal Generative Modelingcited by 7
  18. 2023A Survey of Methods, Challenges and Perspectives in Causalitycited by 6
  19. 2023Predictive Coding beyond Correlationscited by 6
  20. 2024Optimal Transport for Structure Learning Under Missing Datacited by 6
  21. 2024Scalable Differentiable Causal Discovery in the Presence of Latent Confounders with Skeleton Posterior (Extended Version)cited by 6
  22. 2025Position: Causal Machine Learning Requires Rigorous Synthetic Experiments for Broader Adoptioncited by 6
  23. 2025Counterfactual Identifiability via Dynamic Optimal Transportcited by 6
  24. 2022Trust Your nabla: Gradient-based Intervention Targeting for Causal Discoverycited by 5
  25. 2023Order-based Structure Learning with Normalizing Flowscited by 5
  26. 2023Extracting Self-Consistent Causal Insights from Users Feedback with LLMs and In-context Learningcited by 5
  27. 2024Challenges and Considerations in the Evaluation of Bayesian Causal Discoverycited by 5
  28. 2022NeurIPS Competition Instructions and Guide: Causal Insights for Learning Paths in Educationcited by 4
  29. 2023CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Designcited by 4
  30. 2023Structure Learning with Adaptive Random Neighborhood Informed MCMCcited by 4
  31. 2024Permutation-based Inference for Variational Learning of Directed Acyclic Graphscited by 4
  32. 2024ProDAG: Projected Variational Inference for Directed Acyclic Graphscited by 4
  33. 2024Amortized Inference of Causal Models via Conditional Fixed-Point Iterationscited by 4
  34. 2022Efficient Real-world Testing of Causal Decision Making via Bayesian Experimental Design for Contextual Optimisationcited by 3
  35. 2022On the Sparse DAG Structure Learning Based on Adaptive Lassocited by 3
  36. 2023Knowledge Guided Representation Learning and Causal Structure Learning in Soil Sciencecited by 3
  37. 2023Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimationcited by 3
  38. 2023Learned Causal Method Predictioncited by 3

50 most cited of 95 All 95 (JSON)