[DL輪読会]Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolution
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2018/1/26 Deep Learning JP: http://deeplearning.jp/seminar-2/
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[DL輪読会]Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolution
- 1. Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolution Judea Pearl 1
- 2. ¤ Pearl AI ¤ 2018/1/11 arXiv ¤ Judea Pearl ¤ ”One of the giants in the field of artificial intelligence" by Prof. Richard Korf ¤ ¤ ¤ ¤ ¤ DL ¤ +DL ¤ 2
- 8. 3 ¤ ¤ 8 The Three Layer Causal Hierarchy Level Typical Typical Questions Examples (Symbol) Activity 1. Association P(y|x) Seeing What is? How would seeing X change my belief inY ? What does a symptom tell me about a disease? What does a survey tell us about the election results? 2. Intervention P(y|do(x), z) Doing Intervening What if? What if I do X? What if I take aspirin, will my headache be cured? What if we ban cigarettes? 3. Counterfactuals P(yx|x′ , y′ ) Imagining, Retrospection Why? Was it X that caused Y ? What if I had acted differently? Was it the aspirin that stopped my headache? Would Kennedy be alive had Oswald not shot him? What if I had not been smok- ing the past 2 years? Figure 1: The Causal Hierarchy. Questions at level i can only be answered if information from level i or higher is available. An extremely useful insight unveiled by the logic of causal reasoning is the existence of a sharp
- 9. association ¤ 3 1 ¤ ¤ ¤ ¤ X Y ¤ ¤ 2 9 !(#|%) x y
- 10. intervention ¤ ¤ ¤ ¤ ¤ ¤ ¤ ¤ 10 !(#|%& ' , )) X=x Z=z y do
- 11. Counterfactuals ¤ 3 ¤ ¤ ¤ ¤ ¤ ¤ 11 !(#$|&' , #' ) * &' + #' X x Y=y
- 12. ¤ ¤ 1 ¤ ¤ ⇒ 3 ¤ 1 12
- 14. ¤ ¤ ¤ ¤ Garry King [Morgan+ 15] “More has been learned about causal inference in the last few decades than the sum total of everything that had been learned about it in all prior recorded history” ¤ Pearl The Causal Revolution [Pearl+ 18] Structural Causal Models SCM 14
- 16. SCM ¤ Assumption Query ¤ Estimand Estimate Fit ¤ SCM 7 16 The SCM deploys three parts 1. Graphical models, 2. Structural equations, and 3. Counterfactual and interventional logic Graphical models serve as a language for representing what we know about the world, counterfactuals help us to articulate what we want to know, while structural equations serve to tie the two together in a solid semantics. ES ES F Assumptions (Graphical model) Data Fit Indices Estimate (Answer to query) Query answering the query) (Recipe for Estimand Inputs Outputs Figure 2: How the SCM “inference engine” combines data with causal model (or assumptions) to produce answers to queries of interest.
- 19. ¤ ¤ ¤ ¤ ¤ ¤ 19
- 20. ¤ ⇒ ¤ ¤ ¤ X Y Z 20 x z y
- 21. ¤ ¤ ¤ ¤ ¤ ¤ 1 Pearl ¤ Do ¤ non-representative samples 21
- 22. ¤ ¤ ¤ ¤ 22
- 23. ¤ D- ⇒ 23
- 25. ¤ AI ¤ = ¤ Friston ¤ ¤ ¤ VAE ¤ Deep Kalman Filter[Krishnan+ 15, 16] ¤ 25