# A Survey of Learning Causality with Data: Problems and Methods

arXiv: https://arxiv.org/abs/1809.09337 · HTML: https://mlsanity.com/paper/1809.09337/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2018.

## Built on by (most cited first, 30 of 37)

- [A Clarification of the Nuances in the Fairness Metrics Landscape](https://mlsanity.com/paper/2106.00467/) (2021)
- [The Causal-Neural Connection: Expressiveness, Learnability, and Inference](https://mlsanity.com/paper/2107.00793/) (2021)
- [Neural Causal Models for Counterfactual Identification and Estimation](https://mlsanity.com/paper/2210.00035/) (2022)
- [Causal Inference-Based Root Cause Analysis for Online Service Systems with Intervention Recognition](https://mlsanity.com/paper/2206.05871/) (2022)
- [Physics-Guided Deep Learning for Dynamical Systems: A Survey](https://mlsanity.com/paper/2107.01272/) (2021)
- [Evaluation Methods and Measures for Causal Learning Algorithms](https://mlsanity.com/paper/2202.02896/) (2022)
- [Counterfactual Learning on Graphs: A Survey](https://mlsanity.com/paper/2304.01391/) (2023)
- [A Survey of Deep Reinforcement Learning in Recommender Systems: A Systematic Review and Future Directions](https://mlsanity.com/paper/2109.03540/) (2021)
- [A Review of the Role of Causality in Developing Trustworthy AI Systems](https://mlsanity.com/paper/2302.06975/) (2023)
- [Prescriptive Process Monitoring Under Resource Constraints: A Causal Inference Approach](https://mlsanity.com/paper/2109.02894/) (2021)
- [When to intervene? Prescriptive Process Monitoring Under Uncertainty and Resource Constraints](https://mlsanity.com/paper/2206.07745/) (2022)
- [Deep Causal Learning: Representation, Discovery and Inference](https://mlsanity.com/paper/2211.03374/) (2022)
- [Causal Mediation Analysis with Hidden Confounders](https://mlsanity.com/paper/2102.11724/) (2021)
- [Multi-objective Explanations of GNN Predictions](https://mlsanity.com/paper/2111.14651/) (2021)
- [Inductive Granger Causal Modeling for Multivariate Time Series](https://mlsanity.com/paper/2102.05298/) (2021)
- [Quant 4.0: Engineering Quantitative Investment with Automated, Explainable and Knowledge-driven Artificial Intelligence](https://mlsanity.com/paper/2301.04020/) (2022)
- [Estimating Causal Effects of Multi-Aspect Online Reviews with Multi-Modal Proxies](https://mlsanity.com/paper/2112.10274/) (2021)
- [Effects of Multi-Aspect Online Reviews with Unobserved Confounders: Estimation and Implication](https://mlsanity.com/paper/2110.01746/) (2021)
- [Generalized Causal Tree for Uplift Modeling](https://mlsanity.com/paper/2202.02416/) (2022)
- [Causal Reasoning in Software Quality Assurance: A Systematic Review](https://mlsanity.com/paper/2408.17183/) (2024)
- [A Causal Research Pipeline and Tutorial for Psychologists and Social Scientists](https://mlsanity.com/paper/2206.05175/) (2022)
- [VAC2: Visual Analysis of Combined Causality in Event Sequences](https://mlsanity.com/paper/2206.05420/) (2022)
- [Using causal inference to avoid fallouts in data-driven parametric analysis: a case study in the architecture, engineering, and construction industry](https://mlsanity.com/paper/2309.11509/) (2023)
- [CURLS: Causal Rule Learning for Subgroups with Significant Treatment Effect](https://mlsanity.com/paper/2407.01004/) (2024)
- [PORCA: Root Cause Analysis with Partially Observed Data](https://mlsanity.com/paper/2407.05869/) (2024)
- [Causal Discovery from Sparse Time-Series Data Using Echo State Network](https://mlsanity.com/paper/2201.02933/) (2022)
- [Causal Effect Estimation using identifiable Variational AutoEncoder with Latent Confounders and Post-Treatment Variables](https://mlsanity.com/paper/2408.07219/) (2024)
- [Finding Counterfactual Evidences for Node Classification](https://mlsanity.com/paper/2505.11396/) (2025)
- [DODO: Causal Structure Learning with Budgeted Interventions](https://mlsanity.com/paper/2510.08207/) (2025)
- [Machine Learning Prescriptive Canvas for Optimizing Business Outcomes](https://mlsanity.com/paper/2206.10333/) (2022)

## Abstract (arXiv)

This work considers the question of how convenient access to copious data impacts our ability to learn causal effects and relations. In what ways is learning causality in the era of big data different from -- or the same as -- the traditional one? To answer this question, this survey provides a comprehensive and structured review of both traditional and frontier methods in learning causality and relations along with the connections between causality and machine learning. This work points out on a case-by-case basis how big data facilitates, complicates, or motivates each approach.