# Applied Causal Inference Powered by ML and AI

arXiv: https://arxiv.org/abs/2403.02467 · HTML: https://mlsanity.com/paper/2403.02467/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 61 of its 302 references that are in the map.
- First posted on arXiv 4 Mar 2024.

## Builds on (most cited first, 30 of 61)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [An Introduction to Variational Autoencoders](https://mlsanity.com/paper/1906.02691/) (2019)
- [Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations](https://mlsanity.com/paper/1811.12359/) (2018)
- [Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution](https://mlsanity.com/paper/2202.10054/) (2022)
- [Estimation and Inference of Heterogeneous Treatment Effects using Random Forests](https://mlsanity.com/paper/1510.04342/) (2015)
- [Error bounds for approximations with deep ReLU networks](https://mlsanity.com/paper/1610.01145/) (2016)
- [Nonparametric regression using deep neural networks with ReLU activation function](https://mlsanity.com/paper/1708.06633/) (2017)
- [AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data](https://mlsanity.com/paper/2003.06505/) (2020)
- [Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them](https://mlsanity.com/paper/1903.03862/) (2019)
- [Deep learning: a statistical viewpoint](https://mlsanity.com/paper/2103.09177/) (2021)
- [Universal Approximation with Deep Narrow Networks](https://mlsanity.com/paper/1905.08539/) (2019)
- [Statistics of Robust Optimization: A Generalized Empirical Likelihood Approach](https://mlsanity.com/paper/1610.03425/) (2016)
- [To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks](https://mlsanity.com/paper/1903.05987/) (2019)
- [Orthogonal Statistical Learning](https://mlsanity.com/paper/1901.09036/) (2019)
- [Identifying Causal Effects With Proxy Variables of an Unmeasured Confounder](https://mlsanity.com/paper/1609.08816/) (2016)
- [Dive into Deep Learning](https://mlsanity.com/paper/2106.11342/) (2021)
- [Confidence Intervals and Hypothesis Testing for High-Dimensional Regression](https://mlsanity.com/paper/1306.3171/) (2013)
- [Semiparametric proximal causal inference](https://mlsanity.com/paper/2011.08411/) (2020)
- [Orthogonal Random Forest for Causal Inference](https://mlsanity.com/paper/1806.03467/) (2018)
- [Causal Inference Under Unmeasured Confounding With Negative Controls: A Minimax Learning Approach](https://mlsanity.com/paper/2103.14029/) (2021)
- [Conditional Density Estimation with Neural Networks: Best Practices and Benchmarks](https://mlsanity.com/paper/1903.00954/) (2019)
- [Converting High-Dimensional Regression to High-Dimensional Conditional Density Estimation](https://mlsanity.com/paper/1704.08095/) (2017)
- [Foundations of Reinforcement Learning and Interactive Decision Making](https://mlsanity.com/paper/2312.16730/) (2023)
- [Policy learning "without" overlap: Pessimism and generalized empirical Bernstein's inequality](https://mlsanity.com/paper/2212.09900/) (2022)

## Built on by (most cited first, 30 of 30)

- [Predictive Query Language: A Domain-Specific Language for Predictive Modeling on Relational Databases](https://mlsanity.com/paper/2602.09572/) (2026)
- [Mining Causality: AI-Assisted Search for Instrumental Variables](https://mlsanity.com/paper/2409.14202/) (2024)
- [Orthogonal Causal Calibration](https://mlsanity.com/paper/2406.01933/) (2024)
- [Learning Decision Policies with Instrumental Variables through Double Machine Learning](https://mlsanity.com/paper/2405.08498/) (2024)
- [A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of Poverty](https://mlsanity.com/paper/2406.02584/) (2024)
- [Double Machine Learning for Causal Inference under Shared-State Interference](https://mlsanity.com/paper/2504.08836/) (2025)
- [Overlap-weighted orthogonal meta-learner for treatment effect estimation over time](https://mlsanity.com/paper/2510.19643/) (2025)
- [Which Imputation Fits Which Feature Selection Method? A Survey-Based Simulation Study](https://mlsanity.com/paper/2412.13570/) (2024)
- [Causal-EPIG: A Prediction-Oriented Active Learning Framework for CATE Estimation](https://mlsanity.com/paper/2509.21866/) (2025)
- [Demistifying Inference after Adaptive Experiments](https://mlsanity.com/paper/2405.01281/) (2024)
- [Multivariate root-n-consistent smoothing parameter free matching estimators and estimators of inverse density weighted expectations](https://mlsanity.com/paper/2407.08494/) (2024)
- [Double Machine Learning meets Panel Data -- Promises, Pitfalls, and Potential Solutions](https://mlsanity.com/paper/2409.01266/) (2024)
- [Detecting clinician implicit biases in diagnoses using proximal causal inference](https://mlsanity.com/paper/2501.16399/) (2025)
- [Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling](https://mlsanity.com/paper/2505.01145/) (2025)
- [Strategic A/B testing via Maximum Probability-driven Two-armed Bandit](https://mlsanity.com/paper/2506.22536/) (2025)
- [Robust estimation of heterogeneous treatment effects in randomized trials leveraging external data](https://mlsanity.com/paper/2507.03681/) (2025)
- [Functional Natural Policy Gradients](https://mlsanity.com/paper/2603.28681/) (2026)
- [Decision-Focused Learning: When and Why Traditional Prediction Models Fail](https://mlsanity.com/paper/2606.21773/) (2026)
- [Triple/Debiased Lasso for Statistical Inference of Conditional Average Treatment Effects](https://mlsanity.com/paper/2403.03240/) (2024)
- [Causal Inference with Double/Debiased Machine Learning for Evaluating the Health Effects of Multiple Mismeasured Pollutants](https://mlsanity.com/paper/2410.07135/) (2024)
- [Removing Spurious Correlation from Neural Network Interpretations](https://mlsanity.com/paper/2412.02893/) (2024)
- [CAST: Time-Varying Treatment Effects with Application to Chemotherapy and Radiotherapy on Head and Neck Squamous Cell Carcinoma](https://mlsanity.com/paper/2505.06367/) (2025)
- [Shrinkage-Based Regressions with Many Related Treatments](https://mlsanity.com/paper/2507.01202/) (2025)
- [Geopolitics, Geoeconomics, and Sovereign Risk: Different Shocks, Different Channels](https://mlsanity.com/paper/2510.12416/) (2025)
- [Beyond Interaction Effects: Two Logics for Studying Population Inequalities](https://mlsanity.com/paper/2601.04223/) (2025)
- [Cross-Fitting-Free Debiased Machine Learning with Multiway Dependence](https://mlsanity.com/paper/2602.11333/) (2026)
- [Which Alert Removals are Beneficial?](https://mlsanity.com/paper/2603.21322/) (2026)
- [Nonparametric Instrumental Variable Analysis Without Structural Equations: Debiased Inference on Functionals of Inverse Problems with No Solutions](https://mlsanity.com/paper/2604.24660/) (2026)
- [Covariate Balancing and Riesz Regression Should Be Guided by the Neyman Orthogonal Score in Debiased Machine Learning](https://mlsanity.com/paper/2605.06386/) (2026)
- [Covariate Selection for Doubly Robust Double/debiased Machine Learning Estimators for Causal Inference](https://mlsanity.com/paper/2609.17238/) (2026)

## 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.