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Graphical Causal Models: Determining Causes from Observations William Marsh Risk Assessment and Decision Analysis (RADAR) Computer Science 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/ 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 Bayesian Nets 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 Bayes’ Theorem P( A, B) P( A | B).P( B) P( B | A).P( A) Joint probability P( A | B) P( B | A).P( A) Revised belief about A, given evidence B Prior probability of A Factor to update belief about A, given evidence B Bayes’ Theorem (Made Easy) yes, no Infection rate: P(I) = 1% Infection False positive P(T=pos|I=no) = 5% Negligible false negative pos, neg Test A person has a positive test result How likely is it they are infected? 17% Medical Uses of BNs Diagnosis Prediction Differential diagnosis from symptoms Likely outcome Building a BN From expert knowledge expert system From data data mining Beyond Bayesian Networks Cause versus Association Infection Fever ? or Fever Joint probability same: Infection P( I , F ) P ( F | I ).P( I ) P ( I | F ).P( F ) Both represent fever infection association ‘Causal model’ has arrow from cause to effect Causal Induction Discover causal relationships from data Sometimes distinguishable A B C A B C … different conditional independence 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 Estimating Causal Effects Suppose A is a cause of B A B What is the causal effect? Is it p(B | A) ? Benefits of Sports? intelligence sport Is there a relationship between sport and exam success? exam result Data available ‘Intelligence’ correlate Is this the correct test? P(exam=pass|sport) > P(exam=pass| no-sport) Benefits of Sports? intelligence observe sport exam result p(pass|sport) > p(pass| no-sport) 73% When we condition on ‘sport’ 67% Probability for ‘exam result’ Probability for ‘intelligence’ changes What if I decide to start sport? Intervention v Observation intelligence change sport exam result Causal effect differs from conditional probability P(pass|do(sport)) < P(pass| do(no sport)) 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 Benefit of Sport intelligence sport (S) attendance (A) exam result (E) New observable variable ‘attendance at lectures’ Causal effect of sport on exam results now identifiable P( E | do( S )) P( A | S ) P( E | S , A).P( S ) A S Estimating Causal Effects Rules to convert causal to statistical questions Causal model Generalises e.g. stratification, potential outcomes Assumptions: a causal model Some assumptions may be testable Some variables observed, others not measured Some causal effects identifiable Challenges Causal models for complex applications Statistical implications 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? DIADEM Project 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 Cluster Activities and Outcomes Engage stakeholders and build a community: A road map: data and information Creation of a community web-site and forum Meetings with potential ‘problem holders’ Workshops Follow-up proposal A self-sustaining website – health data analytics Summary Bayesian networks Causal induction Prediction and diagnosis Join the DIADEM cluster Identify (some) causal relationships from (lots of) data Causal effects Experimental results from … … non-experimental data … assumptions (causal model)