Causal Inference Engine in Kotlin

Causal Inference Engine based in the paper[1] and implemented in Koltin using Probability Trees.

According the paper, probability trees are one of the simplest models of causal generative processes. They possess clean semantics and -- unlike causal Bayesian networks -- they can represent context-specific causal dependencies, which are necessary for e.g. causal induction. Yet, they have received little attention from the AI and ML community.

[1] Algorithms for Causal Reasoning in Probability Trees
Genewein et al. (2020)
DeepMind
https://arxiv.org/abs/2010.12237

Transcript

1. Causal Inference
   Engine
   Programming AI with Kotlin
   @davilagrau
   

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2. 1. Schedule
   AI paradigm programmed in Kotlin
   ➔ Introduction
   almo, open source and community
   ➔ Kotlin AI
   Java developers’ dream of a better
   programming language.
   ➔ Curve fitting vs Strong AI
   Machines with the sense of humans: mind
   over data
   ➔ Causal Inference Engine
   Answering the simplest question: Why?
   

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3. Introduction:
   a.l.m.o
   Opensource
   Dev Communities
   

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4. A few words about me...
   Andres-Leonardo Martinez-Ortiz a.k.a almo is a member of the
   Google Engineering team, leading Google Developer Relations
   worldwide.
   Based in Zurich, he drives the success of Google's developer
   products and the Open Web by creating a thriving ecosystem of
   developers.
   Nurturing developers experts and partners in large companies,
   startups, universities and enterprises, almo fosters open standards
   and Google technologies.
   almo is also a member of IEEE, ACM, Linux Foundation and
   Computer Society.
   @davilagrau
   almo.dev
   almo
   

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5. Developers came together
   from all corners of the
   world to innovate,
   find connection,
   and solve
   problems*, using
   Internet and Open Source.
   *Octoverse 2020
   

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6. Photo by Perry Grone on Unsplash
   Compartir Aprendiendo
   Aprender Enseñando
   Enseñar Aprendiendo
   

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7. Koltin AI?
   

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8. Why Kotlin?
   Easy learning curve
   Java interoperability
   Ecosystem support
   Educational resources:
   ● Kotlin Koans
   Multi platform
   The language of Android
   and server side, web or
   native applications
   Academically recognized
   Popular in the industry
   Multi paradigm
   Concurrent programming
   including corrutines and CSP
   Object oriented
   Higher order functions and
   Lambda functions
   

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9. Curve fitting
   vs Strong AI
   Photo by Alora Griffiths on Unsplash
   

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10. CSAT for computers,
    emperors and their
    new minds & Chinese
    rooms
    Thinking about intelligence:
    Philosophical foundations.
    Photo by K. Mitch Hodge on Unsplash
    

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11. Superintelligent AI
    May Be Impossible to
    Control.
    Not even possible to know if an AI is
    superintelligent.
    https://spectrum.ieee.org/tech-talk/robotics/artificial-intelligence/super-artificialintelligence
    January 2021
    

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12. Counterfactuals
    Activities: imagining, retrospective,
    understanding.
    What if I have done… ? Why?
    Was X the cause? Y? What if
    X has not occurred? What if I
    acted differently?
    Intervention
    Activities: doing, intervening.
    What if I do… ? How? What
    would Y be if I do X? How can
    I make Y happen?
    Association
    Activities: seeing, observing.
    What if I see…? How are the
    variables related? How would
    seeing X change my belief of Y?
    The Ladder
    of Causation
    

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13. Causal
    Inference
    Engine
    Knowledge
    Assumptions Causal Model
    Testable
    Implication
    Query
    Can we
    answer?
    Back to
    2 and 3
    Estimand
    (Recipe)
    Data
    Statistical
    Estimation
    Estimate
    (Answer)
    1
    2 3 4
    5
    7 8
    9
    6
    Yes
    No
    Inference
    Engine
    

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14. Representing Knowledge in a
    Uncertain World
    Bayesian networks vs
    causality inference
    Mind over data
    “Causation is a language with
    which one can talk efficiently
    about certain structures of
    relevance relationships.”
    (1988)
    “The words embarrass
    me today ”
    (2018)
    

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15. Causal Inference in
    Probability Trees
    Algorithms for Causal
    Reasoning in Probability Trees
    Genewein et al. (2020)
    DeepMind
    https://arxiv.org/abs/1911.10500
    

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16. Definitions
    Algorithms for Causal
    Reasoning in Probability Trees
    Genewein et al. (2020)
    DeepMind
    https://arxiv.org/abs/2010.12237
    

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17. Probability Trees Recursive definition
    Node n is a tuple n = (u,Ｓ,Ｃ)
    ● Id
    ● Statements list
    ● Transition list
    Transition list is a tuple (,) ∈
    [0,1]xN
    ● transition probability
    ● Node m
    The root:
    ● Node without parents
    ● Statement “O=1”
    A leaf is a node with childs.
    

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18. Events and
    min-cuts
    An event is a collection of total
    realization i.e. path from the root to a
    leaf.
    Formally, an event is a cut δ(, ) where
    the true set and the false set contains
    all the nodes where the event becomes
    true and false respectively.
    Critical nodes are Markov Blanket: all
    variables bound within a path from the
    root to the critical nodes are exogenous
    downstream.
    

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19. Min-cuts Algorithms
    Bug:
    it should be V
    

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20. Causal Events
    Precedence Bug:
    it should be
    
    e
    Bug:
    it should be
    
    e
    Note: the event where Y=1 precedes Z=0
    cannot be stated logically. It is a causal event
    requiring a probability tree.
    

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21. Causal Events
    Conditions Bug:
    it should be q
    Question: What is the probability of the event
    A given that the event B is true?
    Note: Downstream (prediction) or upstream
    (inference)
    

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22. Causal Events
    Interventions
    Question: What is the probability of the event
    A given that the event B was made true?
    Note: only downstream (prediction)
    

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23. Code
    AI Applications in Kotlin
    almo’s github repo
    https://github.com/almo
    https://gist.github.com/almo
    

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24. Data Structure
    

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25. Tree Definition
    

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26. Initialization
    Root
    NodeID:01
    Statement:[O,1]
    Transitions
    Statements:[(X, 0)]
    NodeID:01.0
    P:0.4545
    Statements:[(Y, 0)]
    NodeID:01.0.0
    P:0.4000
    Statements:[(Z, 0)]
    NodeID:01.0.0.0
    P:0.5000
    Statements:[(Z, 1)]
    NodeID:01.0.0.1
    P:0.5000
    Statements:[(Y, 1)]
    NodeID:01.0.1
    P:0.6000
    Statements:[(Z, 1)]
    NodeID:01.0.1.0
    P:0.3333
    Statements:[(Z, 0)]
    NodeID:01.0.1.1
    P:0.6667
    Statements:[(X, 1)]
    NodeID:01.1
    P:0.5455
    Statements:[(Z, 0)]
    NodeID:01.1.0
    P:0.3333
    Statements:[(Y, 0)]
    NodeID:01.1.0.0
    P:0.5000
    Statements:[(Y, 1)]
    NodeID:01.1.0.1
    P:0.5000
    Statements:[(Z, 1)]
    NodeID:01.1.1
    P:0.6667
    Statements:[(Y, 1)]
    NodeID:01.1.1.0
    P:0.2000
    Statements:[(Y, 0)]
    NodeID:01.1.1.1
    P:0.8000
    

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27. Min-cuts
    Algorithms
    Remember:
    it should be V
    

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28. Causal Events
    Precedence
    Bug:
    it should be
    
    e
    Bug:
    it should be
    
    e
    

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29. Causal Events
    Conditions
    Bug:
    it should be q
    

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30. Causa Events
    Conditions
    Bug:
    it should be q
    

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31. Causal Events
    Interventions
    

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32. Causal Events
    Interventions
    

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33. Further details
    Please check the reference[1] for additional
    details and consideration.
    Interesting analysis of the application in
    machine learning can be found in the reference
    [2]
    The Causal Inference Engine is work in
    progress. You can find the code at
    http:/
    /bit.ly/2Mcrh4g
    Update and tech news in my twitter timeline
    @davilagrau
    [1] Algorithms for Causal
    Reasoning in Probability
    Trees
    Genewein et al. (2020)
    DeepMind
    https://arxiv.org/abs/2010.12237
    [2] Causality for
    Machine Learning
    B. Schölkopf (2019)
    DeepMind
    https://arxiv.org/abs/1911.10500
    

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34. Causal Inference
    Engine
    Programming AI with Kotlin
    @davilagrau
    Thank you!
    @davilagrau
    almo.dev
    almo
    

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