2. DECISION MAKING AND WHAT-IF QUESTIONS
▸ What if we gave
propranolol to a
patient with
migraine and
nausea?
Image sources: https://cdn.pixabay.com/photo/2017/01/31/20/41/anatomy-2027131_960_720.png ,https://www.flickr.com/photos/possan/2352842020, http://blog.dana-farber.org/insight/2013/03/how-do-cancer-drugs-block-pathways/
3. DECISION MAKING AND WHAT-IF QUESTIONS
▸ What if we gave
propranolol to a
patient with
migraine and
nausea?
▸ What if the US
withdrew from the
Paris agreement?
Image sources: https://cdn.pixabay.com/photo/2017/01/31/20/41/anatomy-2027131_960_720.png ,https://www.flickr.com/photos/possan/2352842020, http://blog.dana-farber.org/insight/2013/03/how-do-cancer-drugs-block-pathways/
4. DECISION MAKING AND WHAT-IF QUESTIONS
▸ What if we gave
propranolol to a
patient with
migraine and
nausea?
▸ What if the US
withdrew from the
Paris agreement?
▸ What if we used a
certain drug on a
cancerous cell?
Image sources: https://cdn.pixabay.com/photo/2017/01/31/20/41/anatomy-2027131_960_720.png ,https://www.flickr.com/photos/possan/2352842020, http://blog.dana-farber.org/insight/2013/03/how-do-cancer-drugs-block-pathways/
5. DECISION MAKING AND WHAT-IF QUESTIONS
▸ What if we gave
propranolol to a
patient with
migraine and
nausea?
▸ What if the US
withdrew from the
Paris agreement?
▸ What if we used a
certain drug on a
cancerous cell?
▸ What-if questions = causal questions
Image sources: https://cdn.pixabay.com/photo/2017/01/31/20/41/anatomy-2027131_960_720.png ,https://www.flickr.com/photos/possan/2352842020, http://blog.dana-farber.org/insight/2013/03/how-do-cancer-drugs-block-pathways/
6. HOW CAN WE DISCOVER CAUSAL RELATIONS?
▸ How can we discover if propranolol improves migraine?
7. HOW CAN WE DISCOVER CAUSAL RELATIONS?
▸ How can we discover if propranolol improves migraine?
▸ Classical approach: experimentation
▸ Sometimes unethical, unfeasible, too expensive
▸ For example, an ineffective drug
Source: http://catbearding.com/wp-content/uploads/2013/06/cat-hate-it.gif
8. CAUSAL DISCOVERY METHODS
▸ Past 30 years: use also information from observations
▸ For example, using correlations…
9. CAUSAL DISCOVERY METHODS
▸ Past 30 years: use also information from observations
▸ For example, using correlations…
https://xkcd.com/552/
Image source: https://commons.wikimedia.org/wiki/File:Crime.svg
10. CAUSAL DISCOVERY METHODS
▸ Past 30 years: use also information from observations
▸ For example, using correlations…
▸ One correlation does not imply causation, but many
(especially combined with non-correlations) may
▸ Constraint-based causal discovery => Logic
https://xkcd.com/552/
Image source: https://commons.wikimedia.org/wiki/File:Crime.svg
11. CONSTRAINT-BASED CAUSAL DISCOVERY EXAMPLE
▸ Observations of many* patients
▸ Assume no other relevant factors
migraine nausea food poisoning
Y Y N
N Y Y
N N N
… … …
12. CONSTRAINT-BASED CAUSAL DISCOVERY EXAMPLE
▸ Observations of many* patients
▸ Assume no other relevant factors
▸ From data:
▸ Migraine is uncorrelated from food poisoning
▸ For patients with nausea, migraine and food poisoning are
(negatively) correlated
migraine nausea food poisoning
Y Y N
N Y Y
N N N
… … …
13. CONSTRAINT-BASED CAUSAL DISCOVERY EXAMPLE
▸ Observations of many* patients
▸ Assume no other relevant factors
▸ From data:
▸ Migraine is uncorrelated from food poisoning
▸ For patients with nausea, migraine and food poisoning are
(negatively) correlated
▸ Causal relations:
migraine nausea food poisoning
Y Y N
N Y Y
N N N
… … …
MIGRAINE FOOD POISONING
NAUSEA
14. ▸ Given enough data*, the predictions are correct
CONSTRAINT-BASED CAUSAL DISCOVERY
15. ▸ Given enough data*, the predictions are correct
▸ Even with arbitrary unmeasured factors
CONSTRAINT-BASED CAUSAL DISCOVERY
MIGRAINE FOOD POISONING
NAUSEA
STRESS
ICE CREAM SALES
TEMPERATURE
CRIME RATES
16. ▸ What happens if not enough data?
▸ Can we fully exploit multiple datasets under different
conditions?
▸ Given enough data*, the predictions are correct
▸ Even with arbitrary unmeasured factors
CONSTRAINT-BASED CAUSAL DISCOVERY
MIGRAINE FOOD POISONING
NAUSEA
STRESS
OPEN QUESTIONS:
ICE CREAM SALES
TEMPERATURE
CRIME RATES
18. WHAT HAPPENS IF NOT ENOUGH DATA?
▸ Statistical independence tests can make errors
▸ State-of-the-art method resolves some errors, but not very fast
19. WHAT HAPPENS IF NOT ENOUGH DATA?
▸ Statistical independence tests can make errors
▸ State-of-the-art method resolves some errors, but not very fast
▸ Ancestral Causal Inference (ACI)
▸ Faster execution time on simplified problem
▸ Method to score predicted causal relations by confidence
SIMULATED DATA
20. SIMULATED DATA PROTEIN SIGNALLING DATA
WHAT HAPPENS IF NOT ENOUGH DATA?
▸ Statistical independence tests can make errors
▸ State-of-the-art method resolves some errors, but not very fast
▸ Ancestral Causal Inference (ACI)
▸ Faster execution time on simplified problem
▸ Method to score predicted causal relations by confidence
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Cause
Effect
21. ▸ Most approaches learn causal relations on these datasets
separately and then combine the results
HOW CAN WE FULLY EXPLOIT MULTIPLE DATASETS?
22. ▸ Most approaches learn causal relations on these datasets
separately and then combine the results
▸ Joint Causal Inference: framework that can systematically pool
data* from different settings to perform independence tests
TOY EXAMPLE
HOW CAN WE FULLY EXPLOIT MULTIPLE DATASETS?
migraine nausea food poisoning
Y Y N
N Y Y
N N N
… … …
migraine nausea food poisoning
Y Y N
N Y Y
… … …
Patients prescribed with propranolol
Patients
MIGRAINE FOOD POISONING
NAUSEA
PROPRANOLOL
known
23. ▸ Most approaches learn causal relations on these datasets
separately and then combine the results
▸ Joint Causal Inference: framework that can systematically pool
data* from different settings to perform independence tests
▸ More accurate than methods combining tests results
SIMULATED DATATOY EXAMPLE
HOW CAN WE FULLY EXPLOIT MULTIPLE DATASETS?
migraine nausea food poisoning
Y Y N
N Y Y
N N N
… … …
migraine nausea food poisoning
Y Y N
N Y Y
… … …
Patients prescribed with propranolol
Patients
MIGRAINE FOOD POISONING
NAUSEA
PROPRANOLOL
known
25. CONCLUSIONS
▸ Causal inference has several important applications (e.g.
systems biology)
▸ This thesis discusses methods that:
1. Improve the scalability of causal inference under uncertainty
2. Score predicted causal relations by confidence
3. Infer causal relations jointly from all available datasets
26. CONCLUSIONS
▸ Causal inference has several important applications (e.g.
systems biology)
▸ This thesis discusses methods that:
1. Improve the scalability of causal inference under uncertainty
2. Score predicted causal relations by confidence
3. Infer causal relations jointly from all available datasets
▸ Not mentioned in the talk:
▸ A more scalable implementation of Probabilistic Soft Logic
▸ Future work: apply it to causal inference?