mlsanityData to 7 Oct 2026

Applied Causal Inference Powered by ML and AI

arXiv 2403.02467 ↗First posted 4 Mar 2024econ.EM cs.LG stat.MEcited by 30

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Abstract (arXiv)

An introduction to the emerging fusion of machine learning and causal inference. The book presents ideas from classical structural equation models (SEMs) and their modern AI equivalent, directed acyclical graphs (DAGs) and structural causal models (SCMs), and covers Double/Debiased Machine Learning methods to do inference in such models using modern predictive tools.

Key facts. Cited by 30 papers in the map (data through 7 Oct 2026). Builds on 61 of its 302 references that are in the map. First posted on arXiv 4 Mar 2024.

Builds on

61 of 302 references.

References · 61

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  4. 2020Language Models are Few-Shot Learnerscited by 32,199
  5. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  6. 2018Deep contextualized word representationscited by 2,285
  7. 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,304
  8. 2019An Introduction to Variational Autoencoderscited by 997
  9. 2018Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representationscited by 929
  10. 2022Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distributioncited by 722
  11. 2015Estimation and Inference of Heterogeneous Treatment Effects using Random Forestscited by 597
  12. 2016Error bounds for approximations with deep ReLU networkscited by 567
38 more
  1. 2017Nonparametric regression using deep neural networks with ReLU activation functioncited by 431
  2. 2020AutoGluon-Tabular: Robust and Accurate AutoML for Structured Datacited by 368
  3. 2019Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Themcited by 268
  4. 2021Deep learning: a statistical viewpointcited by 239
  5. 2019Universal Approximation with Deep Narrow Networkscited by 198
  6. 2016Statistics of Robust Optimization: A Generalized Empirical Likelihood Approachcited by 182
  7. 2019To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Taskscited by 167
  8. 2019Orthogonal Statistical Learningcited by 150
  9. 2016Identifying Causal Effects With Proxy Variables of an Unmeasured Confoundercited by 138
  10. 2021Dive into Deep Learningcited by 111
  11. 2013Confidence Intervals and Hypothesis Testing for High-Dimensional Regressioncited by 78
  12. 2020Semiparametric proximal causal inferencecited by 58
  13. 2018Orthogonal Random Forest for Causal Inferencecited by 51
  14. 2021Causal Inference Under Unmeasured Confounding With Negative Controls: A Minimax Learning Approachcited by 43
  15. 2019Conditional Density Estimation with Neural Networks: Best Practices and Benchmarkscited by 37
  16. 2017Converting High-Dimensional Regression to High-Dimensional Conditional Density Estimationcited by 34
  17. 2023Foundations of Reinforcement Learning and Interactive Decision Makingcited by 33
  18. 2022Policy learning "without" overlap: Pessimism and generalized empirical Bernstein's inequalitycited by 32
  19. 2019Machine Learning Estimation of Heterogeneous Treatment Effects with Instrumentscited by 29
  20. 2017Orthogonal Machine Learning: Power and Limitationscited by 28
  21. 2019Semi-Parametric Efficient Policy Learning with Continuous Actionscited by 26
  22. 2021Long Story Short: Omitted Variable Bias in Causal Machine Learningcited by 23
  23. 2023Deep Learning Meets Sparse Regularization: A Signal Processing Perspectivecited by 21
  24. 2022What's Trending in Difference-in-Differences? A Synthesis of the Recent Econometrics Literaturecited by 18
  25. 2017Causal Inference: A Missing Data Perspectivecited by 14
  26. 2020Estimation and Inference with Trees and Forests in High Dimensionscited by 14
  27. 2021LinCDE: Conditional Density Estimation via Lindsey's Methodcited by 13
  28. 2015Uniformly Valid Post-Regularization Confidence Regions for Many Functional Parameters in Z-Estimation Frameworkcited by 12
  29. 2018Spectral Deconfounding via Perturbed Sparse Linear Modelscited by 12
  30. 2021A Large Scale Benchmark for Individual Treatment Effect Prediction and Uplift Modelingcited by 12
  31. 2022Treatment Effect Risk: Bounds and Inferencecited by 12
  32. 2013Uniform Post Selection Inference for LAD Regression and Other Z-estimation problemscited by 11
  33. 2024Hyperparameter Tuning for Causal Inference with Double Machine Learning: A Simulation Studycited by 11
  34. 2014Inference in High Dimensional Panel Models with an Application to Gun Controlcited by 8
  35. 2024DoubleMLDeep: Estimation of Causal Effects with Multimodal Datacited by 8
  36. 2015A lava attack on the recovery of sums of dense and sparse signalscited by 6
  37. 2018Regression Discontinuity Designs Using Covariatescited by 6
  38. 2019A Practical Introduction to Regression Discontinuity Designs: Foundationscited by 6

50 most cited of 61 All 61 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 11; 2025: 11; 2026: 8.

30 papers.

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  1. 2026Predictive Query Language: A Domain-Specific Language for Predictive Modeling on Relational Databasescited by 8
  2. 2024Mining Causality: AI-Assisted Search for Instrumental Variablescited by 7
  3. 2024Learning Decision Policies with Instrumental Variables through Double Machine Learningcited by 5
  4. 2024Orthogonal Causal Calibrationcited by 5
  5. 2024A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of Povertycited by 5
  6. 2025Double Machine Learning for Causal Inference under Shared-State Interferencecited by 4
  7. 2025Overlap-weighted orthogonal meta-learner for treatment effect estimation over timecited by 3
  8. 2024Which Imputation Fits Which Feature Selection Method? A Survey-Based Simulation Studycited by 2
  9. 2025Causal-EPIG: A Prediction-Oriented Active Learning Framework for CATE Estimationcited by 2
  10. 2024Demistifying Inference after Adaptive Experimentscited by 1
  11. 2024Multivariate root-n-consistent smoothing parameter free matching estimators and estimators of inverse density weighted expectationscited by 1
  12. 2024Double Machine Learning meets Panel Data -- Promises, Pitfalls, and Potential Solutionscited by 1
18 more
  1. 2025Detecting clinician implicit biases in diagnoses using proximal causal inferencecited by 1
  2. 2025Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modelingcited by 1
  3. 2025Strategic A/B testing via Maximum Probability-driven Two-armed Banditcited by 1
  4. 2025Robust estimation of heterogeneous treatment effects in randomized trials leveraging external datacited by 1
  5. 2026Functional Natural Policy Gradientscited by 1
  6. 2026Decision-Focused Learning: When and Why Traditional Prediction Models Failcited by 1
  7. 2024Triple/Debiased Lasso for Statistical Inference of Conditional Average Treatment Effectscited by 0
  8. 2024Causal Inference with Double/Debiased Machine Learning for Evaluating the Health Effects of Multiple Mismeasured Pollutantscited by 0
  9. 2024Removing Spurious Correlation from Neural Network Interpretationscited by 0
  10. 2025CAST: Time-Varying Treatment Effects with Application to Chemotherapy and Radiotherapy on Head and Neck Squamous Cell Carcinomacited by 0
  11. 2025Shrinkage-Based Regressions with Many Related Treatmentscited by 0
  12. 2025Geopolitics, Geoeconomics, and Sovereign Risk: Different Shocks, Different Channelscited by 0
  13. 2025Beyond Interaction Effects: Two Logics for Studying Population Inequalitiescited by 0
  14. 2026Cross-Fitting-Free Debiased Machine Learning with Multiway Dependencecited by 0
  15. 2026Which Alert Removals are Beneficial?cited by 0
  16. 2026Nonparametric Instrumental Variable Analysis Without Structural Equations: Debiased Inference on Functionals of Inverse Problems with No Solutionscited by 0
  17. 2026Covariate Balancing and Riesz Regression Should Be Guided by the Neyman Orthogonal Score in Debiased Machine Learningcited by 0
  18. 2026Covariate Selection for Doubly Robust Double/debiased Machine Learning Estimators for Causal Inferencecited by 0