General public talk: "Logics for causal inference under uncertainty"
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PhD defense general public talk. Thesis: http://dare.ubvu.vu.nl/handle/1871/55267
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General public talk: "Logics for causal inference under uncertainty"
- 1. LOGICS FOR CAUSAL INFERENCE UNDER UNCERTAINTY SARA MAGLIACANE
- 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 Raf Mek PLCg PIP2 PIP3 Erk Akt PKA PKC p38 JNK ACI (ancestral r. + indep. <= 1) Raf Mek PLCg PIP2 PIP3 Erk Akt PKA PKC p38 JNK Weighted causes(i,j) Raf Mek PLCg PIP2 PIP3 Erk Akt PKA PKC p38 JNK Raf Mek PLCg PIP2 PIP3 Erk Akt PKA PKC p38 JNK −1000 −500 0 500 1000 Weighted in Raf Mek PLCg PIP2 PIP3 Erk Akt Raf Mek PLCg PIP2 PIP3 Erk Akt PKA PKC p38 JNK ACI (causes) Raf Mek PLCg PIP2 PIP3 Erk Akt PKA PKC p38 JNK Raf Mek PLCg PIP2 PIP3 Erk Akt PKA PKC p38 JNK −1000 −500 0 500 1000 FCI Raf Mek PLCg PIP2 PIP3 Erk Akt PKA PKC p38 JNK Raf Mek PLCg PIP2 PIP3 Erk Akt PKA PKC p38 JNK −1000 −500 0 500 1000 CFC Raf Mek PLCg PIP2 PIP3 Erk Akt Raf Mek PLCg PIP2 PIP3 Erk Akt PKA PKC p38 JNK 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
- 24. CONCLUSIONS ▸ Causal inference has several important applications (e.g. systems biology)
- 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?