Speaker |
Ricardo Silva |
|---|---|
Affiliation |
UCL Statistical Science |
Date |
Friday, 23 February 2018 |
Time |
13:00-14:00 |
Location |
Zoom |
Link |
Roberts Building G08 Sir David Davies LT |
Event series |
DeepMind/ELLIS CSML Seminar Series |
Abstract |
Causal inference from observational data requires untestable assumptions. As assumptions may fail, it is important to be able to understand how conclusions vary under different premises. Machine learning methods are particularly good at searching for hypotheses, but they do not always provide ways of expressing a continuum of assumptions from which causal estimands can be proposed. We introduce one family of assumptions and algorithms that can be used to provide alternative explanations for treatment effects. If we have time, I will also discuss some other developments on the integration of observational and interventional data using a nonparametric Bayesian approach. |
Biography |