Pragmatism and Variable Transformations in Causal Modelling

By Sebastian Weichwald. 2019

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

1. Yoshua Bengio
2. Anirudh Goyal
3. Stefan Bauer
4. Francesco Locatello
5. Nan Rosemary Ke
6. Nal Kalchbrenner
7. Bernhard Schölkopf
8. Dominik Janzing

Citations in the corpus

• Toward Causal Representation Learning (2021, background): “Pragmatically, we should aim at learning causal models that are useful for specific sets of tasks of interest [208], [266] on appropriate descriptors (in terms of which causal statements they support) that must either be provided or learned.”
• Towards Causal Representation Learning (2021, background): “Pragmatically, we should aim at learning causal models that are useful for specific sets of tasks of interest [207, 267] on appropriate descriptors (in terms of which causal statements they support) that must either be”