A Survey of Learning Causality with Data: Problems and Methods
By Ruocheng Guo, Lu Cheng, Jundong Li, P. Richard Hahn, Huan Liu. 2018
335 citations recorded by the source corpus.
Subfields: Causal Inference & Discovery
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Causal Inference & Discovery
- Yoshua Bengio
- Anirudh Goyal
- Stefan Bauer
- Francesco Locatello
- Nan Rosemary Ke
- Nal Kalchbrenner
- Bernhard Schölkopf
- Richard Bowden
Citations in the corpus
- Toward Causal Representation Learning (2021, background): “It also led to a range of follow-up work [32], [78], [97], [114], [115], [152], [153], [156], [167], [195], [204], [243], [263], [267], [277], [278], [281], complementing the studies of Bareinboim and Pearl [14], [185], and it inspired a thread of work in the statistics community exploiting…”
- Causality for Machine Learning (2019, background): “The line of work provided rather fruitful (Zhang et al., 2013; Weichwald et al., 2014; Zhang et al., 2015; Gong et al., 2016; Huang et al., 2017; Blöbaum et al., 2016; Guo et al., 2018; Wang et al., 2019; Subbaswamy et al., 2018; Rabanser et al., 2018; Li et al., 2018a; Lipton et al., 2018; Li et al”
- 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 Tutorial On the design, experimentation and application of metaheuristic algorithms to real-World optimization problems (2021, background): “Causal analysis for machine learning models [150, 151] can take a step further in this direction, discriminating which decision variables, when modified throughout the search, lead to major changes in the fitness value.”
- Empowering Things With Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things (2020, background): “Recently, learning causality from observational data has attracted much attention [229].”
- D’ya Like DAGs? A Survey on Structure Learning and Causal Discovery (2021, background): “These include: A discussion of the relevance of causality to machine learning [71, 175, 214]; Commentaries on the nature of causality [143, 160]; alternative reviews on causal inference and causal discovery [69, 84, 92, 230, 268]; reviews with a focus on time-series causal inference and discovery [5”