Accounting for hidden common causes when infering cause and effect from observational data

By David E. Heckerman. 2018

10 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Ioana Bica
2. Ahmed M. Alaa
3. Mihaela van der Schaar
4. Yixin Wang
5. David M. Blei
6. Bernhard Schölkopf
7. Rajesh Ranganath
8. Yoshua Bengio

Citations in the corpus

• Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders (2019, methodology): “Several other methods have been proposed for taking advantage of the multiplicity of assigned causes in the static setting and capture shared latent confounding (Tran & Blei, 2017; Heckerman, 2018; Ranganath & Perotte, 2018).”
• Multiple Causes: A Causal Graphical View (2019, background): “Finally, this paper connects to the growing literature on multiple causal inference (Tran & Blei, 2017; Wang & Blei, 2018; Ranganath & Perotte, 2018; Heckerman, 2018; Janzing & Schölkopf, 2018).”
• The Blessings of Multiple Causes (2018, background): “Heckerman (2018) studies the same scenario with linear regression, where observing many causes makes it possible to account for shared confounders.”