A Survey of Learning Causality with Data
By Ruocheng Guo, Lu Cheng, Jundong Li, P. Richard Hahn, Huan Liu. 2018
184 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Bernhard Schölkopf
- Amir-Hossein Karimi
- Isabel Valera
- Hanwang Zhang
- Xiansheng Hua
- Qianru Sun
- Gilles Barthe
- Huan Liu
Citations in the corpus
- Causal Interpretability for Machine Learning - Problems, Methods and Evaluation (2020, background): “[36] provide a comprehensive review of existing causal inference methods and definitions.”
- A survey of algorithmic recourse: definitions, formulations, solutions, and prospects (2020, background): “For XAI, see [1, 2, 31, 55, 61, 67, 75, 111, 120, 128], for causal and counterfactual inference, see [63, 76, 131, 145, 146, 159, 185], and for optimization, see [32, 137, 166].”
- A Survey on Causal Inference (2020, background): “There is also a survey about learning causality from observational data [52] whose content ranges from inferring the causal graph from observational data, SCM, potential outcome”
- Causal Intervention for Weakly-Supervised Semantic Segmentation (2020, methodology): “Although the Rubin’s potential outcome framework [42] can also be used, as the two are fundamentally equivalent [16, 38], we prefer Pearl’s because it can explicitly introduce the causality in WSSS — every node in the graph can be located and implemented in the WSSS pipeline.”