Gaussian Processes for Independence Tests with Non-iid Data in Causal Inference

By Seth R. Flaxman, Daniel B. Neill, Alex Smola. 2015

44 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology, 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 Artificial Intelligence

1. Bharath K. Sriperumbudur
2. Bernhard Schölkopf
3. Kenji Fukumizu
4. Arthur Gretton
5. Krikamol Muandet
6. Dino Sejdinovic
7. David Lopez-Paz
8. Sarah Filippi

In Molecular Biology

1. Arthur Gretton
2. Kenji Fukumizu
3. Bernhard Schölkopf
4. Bharath K. Sriperumbudur
5. Dino Sejdinovic
6. Krikamol Muandet
7. Alex Smola
8. Le Song

In Causal Inference & Discovery

1. Dino Sejdinovic
2. Krikamol Muandet
3. Bharath K. Sriperumbudur
4. Kenji Fukumizu
5. Bernhard Schölkopf
6. Arthur Gretton
7. Sarah Filippi
8. Qinyi Zhang

Citations in the corpus

• Kernel Mean Embedding of Distributions: A Review and Beyonds (2016, methodology): “In a similar vein, Flaxman et al. (2015a) adopts Gaussian process regression in conditional independence test on non-i.i.d. observations.”
• Large-scale kernel methods for independence testing (2016, background): “2012), causal inference (Peters et al. 2014; Flaxman et al. 2015; Zaremba andAste 2014) and computational linguistics (Nguyen and Eisenstein 2016).”