University of Cambridge > Talks.cam > Statistics > It is hard to be strongly faithful

  Browse A-Z

Log in

University Account External (via Google)

🔐 Log In

Information on

Subscribing to talksdetails Finding a talkdetails Adding a talkdetails Disseminating talksdetails Help and Documentationdetails

ℹī¸ Information

It is hard to be strongly faithful

Download to your calendar using vCal

If you have a question about this talk, please contact .

Many algorithms for inferring causality are based on partial correlation testing. Partial correlations define hypersurfaces in the parameter space of a directed Gaussian graphical model. The volumes obtained by bounding partial correlations play an important role for the performance of causal inference algorithms. By computing these volumes we show that the so-called “strong-faithfulness assumption”, one of the main constraints of many causal inference algorithms, is in fact extremely restrictive, implying fundamental limitations for these algorithms. We then propose an alternative method that involves finding the permutation of the variables that yields the sparsest DAG . In the Gaussian setting, our sparsest permutation (SP) algorithm boils down to determining the permutation with sparsest Cholesky decomposition of the inverse covariance matrix. We prove that the constraints required for our SP algorithm are strictly weaker than strong-faithfulness and are necessary for any causal inference algorithm based on conditional independence testing.

This talk is part of the Statistics series.

This talk is included in these lists:

Note that ex-directory lists are not shown.

Other lists

Level 4 Seminar Rooms, IMS, Addenbrooke's Hospital Online - register here https://www.eventbrite.co.uk/e/integrated-cancer-medicine-seminar-series-tickets-208452395727 Average on weekdays: 8928.53295669 kWh

Other talks

Rothschild Public Lecture: The Quantum Centenary: from Order to Chaos TBC Cooperation for Nature Group Work Group Presentation 1 Title TBC
 

Š 2006-2025 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity