Causal Discovery via Reproducing Kernel Hilbert Space Embeddings

By Zhitang Chen, Kun Zhang, L. Chan, Bernhard Schölkopf. 2014

47 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Probabilistic & Bayesian Methods, Causal Inference & Discovery, Signal Processing

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

In Probabilistic & Bayesian Methods

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

In Causal Inference & Discovery

1. David Lopez-Paz
2. Bharath K. Sriperumbudur
3. Bernhard Schölkopf
4. Krikamol Muandet
5. Kenji Fukumizu
6. Daniel Hernández-Lobato
7. Pablo Morales-Mombiela
8. Alberto Suárez

In Signal Processing

1. Kenji Fukumizu
2. David Lopez-Paz
3. Bharath K. Sriperumbudur
4. Krikamol Muandet
5. Daniel Hernández-Lobato
6. Pablo Morales-Mombiela
7. Alberto Suárez
8. Yi Chang

Citations in the corpus

• Kernel Mean Embedding of Distributions: A Review and Beyonds (2016, methodology): “…(Gretton et al. 2012a), independence and conditional independence tests (Fukumizu et al. 2008, Zhang et al. 2011, Doran et al. 2014), causal inference (Sgouritsa et al. 2013, Chen et al. 2014), adaptive MCMC (Sejdinovic et al. 2014), and approximate Bayesian computation (Fukumizu et al. 2013).”
• Robust Tree-based Causal Inference for Complex Ad Effectiveness Analysis (2015, methodology): “Besides propensity-based causal inference methodologies, structural equation model [5], inference based on decomposition of the joint distribution of the treatment and response variables [22, 11], and before-and-after studies [39] are also used for causal inference from different points of view.”
• Non-linear Causal Inference using Gaussianity Measures (2014, methodology): “In EMD and synthetic data, we follow (Chen et al., 2014) to select the hyper-parameters.”