CADDIS Workshop [Causal Analysis/Diagnosis Decision Information System] New Jersey Water Restoration, Protection, and Planning Working Group Rutgers University, New Jersey May 7, 2008

2 Your guides… Susan Cormier Kate Schofield

3 TimeTopicFacilitator 10:00IntroductionHost 10:10Philosophy and Framework of Causal AnalysisCormier 10:50Analysis and Interpretation of EvidenceSchofield 11:30Identifying the Probable CauseCormier 12:00CADDIS Tour— resources for causal analysisSchofield 12:30Lunch 1:00CADStat Enter data Make a Scatter Plot Cormier 1:30Analysis and Interpretation of Evidence Scatter Plot Box Plot Conditional Probability Analysis Regression and Quantile Regression Schofield/ Cormier 2:30Screening AssessmentSchofield

4 Why Stressor Identification? All states and several tribes use biological assessments to identify whether streams and small rivers are impaired. In many cases, causes of impairment are unknown. To set a TMDL (or otherwise address the problem), you must identify a cause. It’s defensible & reproducible.

5 5 Success stories SITERESULT Bogue Homo, MS Identified low DO, high sediment as probable causes of impairment; developed template to expedite TMDLs Touchet River, WA Recommended sediment reduction & restoration of canopy cover Groundhouse River, MN Identified data needs & secured funding for data collection Long Creek, ME Developed report examining non-point source TMDLs for urban streams Naugatuck River, CT Used applied toxicity tests & modeling to identify toxic effluent as probable cause; TMDL recently approved

6 CADDIS is based on a formal method 2005, 2007… Guidance 2002

7 General and Specific Causation General – Does C cause E? –Does smoking cause lung cancer? –Does dissolved oxygen reduce mayflies in rivers? Case and Cause Specific – Did C cause E? –Did smoking cause lung cancer in Ronald Fisher? –Did low dissolved oxygen reduce mayflies in my river? 8 Case Specific —Which C caused E? –What exposure and/or genetic predisposition caused cancer in Ronald Fisher –What stressor(s) are causing reduced mayflies in my river

8 Physical Interaction Causal agents change affected agents by physical interaction. DirectionalityThe cause precedes its effect Sufficiency Intensity or frequency of contact with the cause is adequate to produce the observed effect Sequential Dependence All effects result from a prior sequence of cause-effect events Coherence of Characteristics Specific causal relationships are consistent with scientific theory Characteristics of Causal Relationships

9 Inference of Causal Characteristics Inference fromDefinition Observation The effect is observed to occur with the cause. ManipulationChanging the cause changes its effect. Prediction Knowledge from empirical observations or mechanistic studies permits reliable prediction of events.

10 Inference from Observation/ Consistency ManipulationPrediction Physical Interaction Directionality Sufficiency Sequential Dependence Coherence of Characteristics Characteristics of physical interactions Inference of Causal Characteristics Provides Evidence of Causal Relationships

11 Inference from Observation/ Consistency ManipulationPredictability Physical Interaction  Spatial/Tempor al Co- occurrence  Manipulation of co- occurrence; e.g., caging experiment  Evidence of exposure or biological mechanism  Verified Prediction Directionality  Temporal Sequence  Manipulation of time order  Verified prediction of time order Sufficiency  Stressor- Response in the Field  Manipulation of duration, concentration, frequency, other factors  Laboratory tests of site media  Stressor response from ecological simulation or physiological models Sequential Dependence  Causal pathway (before and after)  Manipulation of circumstances or pathways leading to the proximate cause  Verified prediction of source, precursor, or subsequent effect  Analogous situations Coherence of Characteristics  Mechanistically plausible  Symptomology  Verified Prediction of Symptom  Analogous stressors Characteristics of physical interactions Inference of Causal Characteristics Provides Evidence of Causal Relationships

12 Types of Evidence used in CADDISCharacteristic Spatial/Temporal Co-occurrence with positive reference sites Physical Interaction Spatial/Temporal Co-occurrence through Time Spatial/Temporal Co-occurrence Upstream Downstream Comparison Evidence of Exposure or Biological Mechanism Temporal Sequence Directionality Stressor-Response Relationship in the Field Sufficiency Stressor-Response from Laboratory Studies Stressor-Response from Ecological Simulation Models Causal Pathway Causal Dependence Symptoms Coherence Mechanistically plausible  Laboratory Tests of Site Media (Manipulation)  Verified Predictions  Manipulation of Exposure  Analogous stressors (Prediction)

13 General and Specific Causation General – Does C cause E? –Ecological knowledge and theory, regional assessment Case and Cause Specific – Did C cause E? –Good for permits, damage assessment costs, reregistration of chemicals 8 Case Specific —Which C caused E? –Identifying an unknown cause

14 Planning Analysis Synthesis

15 Planning Analysis Synthesis Decision/ Action Initiator Common Assessment Process Define the Case List Candidate Causes Evaluate Data from the Case Evaluate Data from Elsewhere Identify Probable Cause Detect or Suspect Biological Impairment Identify and Apportion Sources Management Action: Eliminate or Control Sources, Monitor Results Biological Condition Restored or Protected Stressor Identification CADDIS Stressor Identification Process Problem Resolution

16 Our causal strategy (& some notes) Identify alternative candidate causes Logically eliminate when you can Diagnose when you can Use strength of evidence for the rest Identify most likely cause – Do not claim proof of causation – Use a consistent process – Document evidence & inferences

17 Why use a formal method? To provide a defensible & reproducible evaluation To convince stakeholders To increase confidence that remedial or restoration efforts can improve biological condition To identify causal relationships that are not immediately apparent To prevent biases and other lapses of logic “Science is a way of trying not to fool yourself. The first principle is that you must not fool yourself – and you are the easiest person to fool.” [Feynman 1964]

18 Philosophers of causation David Hume: Causality as associations between events Galileo: Causes of effects are knowable John Stuart Mill: Manipulating cause will change effect Charles Peirce: Inference to the best explanation

19 Philosophers of causation Ronald Fisher: Controlled experiments with replication and randomization Karl Pearson: Need to quantify association between variables Austin Bradford Hill: Causality based on strength of evidence Robert Koch: Postulates for demonstrating causes of diseases

20 Gives you tools & information to make your causal assessment easier You can’t just push a button on CADDIS to determine cause of impairment… – Will need to use your head – Will need data to generate evidence What does CADDIS do?