1. From Correlation to Causation: Lessons for Security & Defense
David Danks
Philosophy // Psychology Heinz College

2. Correlation vs. Causation
Correlation = Using an observation about one factor to predict another Causation = Using an intervention on one factor to influence another
Predict warfighter performance given training? or
Change training to improve performance?
Predict development of cancer cells? or
Give treatment to slow growth of cancer?

3. Correlation vs. Causation
Correlation = Using an observation about one factor to predict another Causation = Using an intervention on one factor to influence another
Correlation ≠ Causation…

4. Correlation vs. Causation
Correlation = Using an observation about one factor to predict another Causation = Using an intervention on one factor to influence another
Correlation ≠ Causation… …but Correlation is correlated with Causation
Correlation is a noisy guide to Causation

5. Correlation vs. Causation

6. Correlation vs. Causation
Classifying & identifying
Informational value of different evidence
Prediction & reasoning given observations
Probable explanations for some event or issue

7. Correlation vs. Causation
Classifying & identifying
Influencing & acting
Informational value of different evidence
Using evidence to guide policy or actions
Prediction & reasoning given observations
Prediction & reasoning given interventions
Probable explanations for some event or issue
Ways to produce or prevent an event or problem

8. Correlation & Causation
Basic strategy:
Causal Structure
Empirical Data
Reasoning & inference
Learning & discovery …

9. Causal representation
Directed Acyclic Graphs:
Graph ⇒ Qualitative causation
Probabilities ⇒ Quantitative causation
Mutually constrain via assumptions
Intuitive, compact “language”
Superior SME elicitation
Psychologically natural
Many natural variants
Knowledge
Studying
Test Score
Rest …

10. Causal reasoning & inference
Given a causal model, computationally efficient methods to:
Predict given evidence or information
“The adversary increased production in this factory; how likely is an imminent attack?”
Construct explanations & troubleshoot
“This radar system failed; what went wrong?”
Design actions/policies & predict outcomes
“If we increase aid to that region, how much does that stabilize the regime?”
And each also with multiple possibilities & other variations …

11. Causal discovery
Idea: Given data (observational or interventional), find causal structures that could plausibly have produced those data
Multiple algorithms implementing this idea
Almost all with long-run guarantees, but variable short-run performance
Standard data analysis methods are often special cases of these algorithms …

12. Causal discovery … Challenges with known solutions:
Multiple (overlapping) variables
Observation of all relevant factors
Mixed variable-types
Linear systems with known noise
Non-stationary causal structure
Unobserved common causes
Selection bias in the data
Similar-but-varying causal structures across individuals
Measurements only of proxies
Undersampled time series with missing, causally relevant variables
Time series causal structures
Equilibrated systems with feedback
Massive (> 1M) number of variables
Unobserved mechanisms … …

13. Causal discovery in action
Which factors influence growth of Spartina grass (in Cape Fear)?
Causal discovery on purely observational data: only pH
Other factors matter, but always mediated by pH
Greenhouse study that randomized key factors: Confirmation!

14. Causal discovery in action
What is the communication structure of the brain?
Many challenges, including:
Very noisy data
Undersampling
Unobserved variables
Recovered (most) known structure from data, plus interesting hypotheses

15. Causal discovery in action
What causes degradation of jet engine performance over time?
Causal discovery of pre-failure causes with aim to adjust maintenance schedules
Testing is ongoing

16. Thanks!