Causal Learning

By Linda Bol, Douglas J. Hacker, Andrew Mattarella-Micke, Sian L. Beilock, N. Seel, C. Rosenstand, Lowell Dean Tong, Christian Burke, Ann N Poncelet, Shawn Ell and 172 more. 2012

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. Bernhard Schölkopf
2. Tania Lombrozo
3. Dominik Janzing
4. Jan Peters
5. Kun Zhang
6. David Lopez-Paz
7. Joris M. Mooij
8. Stefan Bauer

Citations in the corpus

• Causality for Machine Learning (2019, background): “One may thus hypothesize that for causal learning problems (predicing effect from cause), adversarial examples should be impossible, or at least harder to find (Schölkopf, 2017; Kilbertus et al., 2018).”
• Illusions of causality: how they bias our everyday thinking and how they could be reduced (2015, background): “Most of the experiments on how people detect real or illusory causal relationships have used a variation of the same procedure: the contingency judgment task (see special volumes by Shanks et al., 1996; Beckers et al., 2007).”
• Causal-explanatory pluralism: How intentions, functions, and mechanisms influence causal ascriptions. (2010, background): “Many approaches to causal learning, for example, focus on co-occurrence data and probabilistic relationships, often with an implicit or explicit commitment to dependence (Gopnik et al., 2004; Griffiths & Tenenbaum, 2005; see Buehner and Cheng (2005) and Sloman (2005) for reviews).”