Historical perspective in causal structure learning from observational and experimental data
published: Oct. 6, 2014, recorded: December 2013, views: 1529
Download slides: nipsworkshops2013_scheines_experimental_data_01.pdf (250.9 KB)
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
I will briefly introduce graphical causal models, the basic formal representation of causation that is becoming standard in computer science and elsewhere, and then use them to explain how causal discovery depends on the "data collection regime," especially with regards to whether data is collected from passive observation or experimental control. I will then use graphical models and time series to make vivid the serious challenges that still beset causal inference even in "randomized clinical trials," for example blinding and non-compliance.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !