1. Graphical Causal Models: Determining Causes from Observations William Marsh Risk Assessment and Decision Analysis (RADAR) Computer Science

2. RADAR Group, Computer Science Risk Assessment and Decision Analysis Research areas Software engineering, safety, finance, legal A new initiative in medical data analysis: DIADEM Norman Fenton Group leader Martin Neil http://www.dcs.qmul.ac.uk/researchgp/radar/

3. Outline Graphical Causal Models Bayesian networks: prediction or diagnosis Causal induction: learning causes from data Causal effect estimation: strength of causal relationships from data DIADEM project

4. Bayesian Nets

5. Detecting Asthma Exacerbations Aim to assist early detection of asthma episodes in Paediatric A&E Using only data already available electronically Network created by Experts Data

6. Bayes’ Theorem Joint probability Revised belief about A, given evidence B Prior probability of A Factor to update belief about A, given evidence B

7. Bayes’ Theorem (Made Easy) A person has a positive test result How likely is it they are infected? 17% Infection Test yes, no pos, neg False positive P(T=pos|I=no) = 5% Negligible false negative Infection rate: P(I) = 1%

8. Medical Uses of BNs Diagnosis Differential diagnosis from symptoms Prediction Likely outcome Building a BN From expert knowledge  expert system From data  data mining

9. Beyond Bayesian Networks

10. Cause versus Association Both represent fever  infection association ‘Causal model’ has arrow from cause to effect Infection FeverInfection Fever or ? Joint probability same:

11. Causal Induction Discover causal relationships from data Sometimes distinguishable … different conditional independence A BC A BC

12. Causal Induction – Application Discover causal relationships from data Need lots of data Applied to gene regulatory networks Data from micro-array experiments Recent explanation of limitations

13. Estimating Causal Effects Suppose A is a cause of B What is the causal effect? Is it p(B | A) ? A B

14. Benefits of Sports? Is there a relationship between sport and exam success? Data available ‘Intelligence’ correlate Is this the correct test? intelligence sportexam result P(exam=pass|sport) > P(exam=pass| no-sport)

15. Benefits of Sports? When we condition on ‘sport’ Probability for ‘exam result’ Probability for ‘intelligence’ changes What if I decide to start sport? p(pass|sport) > p(pass| no-sport) 73% 67% observe intelligence sportexam result

16. Intervention v Observation Causal effect differs from conditional probability Mostly interested in consequence of change Causal effects can be measured by a Randomised Control Trial Causal effect of sport on exam results not identifiable change P(pass|do(sport)) < P(pass| do(no sport)) intelligence sportexam result

17. Benefit of Sport New observable variable ‘attendance at lectures’ Causal effect of sport on exam results now identifiable sport (S)exam result (E) intelligence attendance (A)

18. Estimating Causal Effects Rules to convert causal to statistical questions Generalises e.g. stratification, potential outcomes Assumptions: a causal model Some assumptions may be testable Causal model Some variables observed, others not measured Some causal effects identifiable Challenges Causal models for complex applications Statistical implications

19. Example Application Royal London trauma service Criteria for activation of the trauma team Aim to prevent unnecessary trauma team calls Extensive records of trauma patient outcomes US study of 1495 admissions proposed new ‘triage’ criteria Significant decrease in overtriage 51%  29% Insignificant increase in undertriage 1%  3% None of the patients undertriaged by new criteria died Does this show safety of new criteria?

20. DIADEM Project

21. Digital Economy in Healthcare Data Information and Analysis for clinical DEcision Making EPSRC Digital Economy Cluster Partnership between solution providers and clinical data analysis problem holders Summarise unsolved data analysis needs, in relation to the analysis techniques available Join the DIADEM cluster

22. Cluster Activities and Outcomes Engage stakeholders and build a community: Creation of a community web-site and forum Meetings with potential ‘problem holders’ Workshops A road map: data and information Follow-up proposal A self-sustaining website – health data analytics

23. Summary Bayesian networks Prediction and diagnosis Causal induction Identify (some) causal relationships from (lots of) data Causal effects Experimental results from … … non-experimental data … assumptions (causal model) Join the DIADEM cluster