William Marsh BNs-to-causal-identificati

Graphical Causal Models: Determining Causes from Observations : 

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

RADAR Group, 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 : 

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 : 

Bayesian Nets

Detecting Asthma Exacerbations : 

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 : 

Bayes’ Theorem Joint probability

Bayes’ Theorem (Made Easy) : 

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%

Medical Uses of BNs : 

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

Beyond Bayesian Networks : 

Beyond Bayesian Networks

Cause versus Association : 

Cause versus Association Both represent fever  infection association ‘Causal model’ has arrow from cause to effect Infection Fever Infection Fever or ?

Causal Induction : 

Causal Induction Discover causal relationships from data Sometimes distinguishable … different conditional independence

Causal Induction – Application : 

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 : 

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

Benefits of Sports? : 

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

Benefits of Sports? : 

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% intelligence sport exam result

Intervention v Observation : 

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 P(pass|do(sport)) < P(pass| do(no sport)) intelligence sport exam result

Benefit of Sport : 

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)

Estimating Causal Effects : 

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

Example Application : 

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 : 

DIADEM Project

Digital Economy in Healthcare : 

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 : 

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

Summary : 

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