The Randomized Causation Coefficient
By David Lopez-Paz, Krikamol Muandet, B. Recht. 2014
39 citations recorded by the source corpus.
We are interested in learning causal relationships between pairs of random variables, purely from observational data. To effectively address this task, the state-of-the-art relies on strong assumptions on the mechanisms mapping causes to effects, such as invertibility or the existence of additive noise, which only hold in limited situations. On the contrary, this short paper proposes to learn how to perform causal inference directly from data, without the need of feature engineering. In particular, we pose causality as a kernel mean embedding classification problem, where inputs are samples from arbitrary probability distributions on pairs of random variables, and labels are types of causal relationships. We validate the performance of our method on synthetic and real-world data against the state-of-the-art. Moreover, we submitted our algorithm to the ChaLearn's "Fast Causation Coefficient Challenge" competition, with which we won the fastest code prize and ranked third in the overall leaderboard.
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
- David Lopez-Paz
- Yoshua Bengio
- Dacheng Tao
- Bernhard Schölkopf
- Sergey Levine
- Quoc V. Le
- Bernt Schiele
- L. Eon Bottou
Citations in the corpus
- Discovering Causal Signals in Images (2016, background): “We also build on the growing literature discussing the discovery of causal relationships from observational data [10, 19, 17, 1].”
- From Dependence to Causation (2016, methodology): “We introduce generalized distillation (Lopez-Paz et al., 2016a), a framework to learn from multiple data modalities and machines semisuper- CHAPTER 1.”