Name
Abstract | ||
|---|---|---|
We describe a method for removing the effect of confounders to reconstruct a latent quantity of interest. The method, referred to as "half-sibling regression," is inspired by recent work in causal inference using additive noise models. We provide a theoretical justification, discussing both independent and identically distributed as well as time series data, respectively, and illustrate the potential of the method in a challenging astronomy application. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1073/pnas.1511656113 | PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA |
Keywords | Field | DocType |
machine learning, causal inference, astronomy, exoplanet detection, systematic error modeling | Half-sibling,Causal inference,Time series,Confounding,Regression,Independent and identically distributed random variables,Statistics,Mathematics | Journal |
Volume | Issue | ISSN |
113 | 27 | 0027-8424 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bernhard Schölkopf | 1 | 23120 | 3091.82 |
| David W. Hogg | 2 | 44 | 4.55 |
| Dun Wang | 3 | 0 | 0.34 |
| Daniel Foreman-Mackey | 4 | 0 | 0.34 |
| Dominik Janzing | 5 | 723 | 65.30 |
| Carl-Johann Simon-Gabriel | 6 | 37 | 3.61 |
| Jonas Peters | 7 | 505 | 31.25 |