1. Two Solutions to the Main Problem of Probabilistic Causality: a Comparison by Donald Gillies, University College London

2. Contents 1. Introduction 2. Causality – Generic and Indeterminate 3. Probabilistic Causality and its Main Problem 4. A Suggested Solution to the Main Problem 5. Pearl's Alternative Approach

3. Generic Causality A causes B is said to be generic if it can be instantiated on different occasions. Example: Throwing a stone over the cliff edge causes it to fall into the sea. (1)

4. Single-case Causality A causes B is said to be single-case if it applies to only one instance. Example: A heart attack caused Mr Smith's death. (2)

5. Determinate Causality A causes B is said to be determinate, if, whenever A occurs, it is followed by B. Example: Throwing a stone over the cliff edge causes it to fall into the sea. (1)

6. Indeterminate Causality A causes B is said to be indeterminate, if A can occur without always being followed by B. Example: Smoking causes lung cancer. (3) (Only about 5% of smokers get lung cancer.)

7. Annual Death Rate for Lung Cancer per 100,000 Men, standardised for age Non-smokers10 Smokers104 1-14 gms tobacco per day52 14-24 gms tobacco per day106 25 gms tobacco per day or more224 (A cigarette is roughly equivalent to 1 gm of tobacco) Source: Doll and Peto, 1976, p. 1527.

8. Causality Probability Connection Principle (CPCP) If A causes B, the P(B | A) > P(B | ¬A) (CPCP)

9. A Multi-Causal Fork

10. CPCP in the case of a Multi- Causal Fork with Binary Variables If X causes Z, then P(Z = 1 | X = 1) > P(Z = 1 | X = 0) (*)

11. Von Mises' Principle FIRST THE COLLECTIVE – THEN THE PROBABILITY

12. Two Reference Class Principles FIRST THE REFERENCE CLASS – THEN THE PROBABILITY FIRST THE REFERENCE CLASS – THEN THE CAUSALITY

13. Proposal for Solving Hesslow's Problem Divide S into two disjoint reference classes S & (Y = 0) and S & (Y = 1). Claim that (*) holds for each of these two reference classes but not necessarily for S itself.

14. Pearl on Probabilistic Causality (PC) “... the PC program is known mainly for the difficulties it has encountered, rather than its achievements. This section explains the main obstacle that has kept PC at bay for over half a century, and demonstrates how the structural theory of causation clarifies relationships between probabilities and causes” (2011, p. 714)

15. Need for the do-Calculus “The way philosophers tried to capture this relationship, using inequalities such as P(E | C) > P(E) was misguided from the start – counterfactual 'raising' cannot be reduced to evidential 'raising' or 'raising by conditioning'. The correct inequality, according to the structural theory..., should read: P(E | do(C)) > P(E) where do(C) stands for an external intervention that compels the truth of C. The conditional probability P(E | C)... represents a probability resulting from a passive observation of C, and rarely coincides with P(E | do(C)) (Pearl, 2011, p. 715)

16. CPCP using do-Calculus If X causes Z, then P(Z = 1 | do(X = 1)) > P(Z = 1 | do(X = 0)) (**)

17. Latent Structural Causal Models “If a set PAi in a model is too narrow, there will be disturbance terms that influence several variables simultaneously and the Markov property will be lost. Such disturbances will be treated explicitly as 'latent' variables.... Once we acknowledge the existence of latent variables and represent their existence explicitly as nodes in a graph, the Markov property is restored.” (Pearl, 2000, p. 44)

18. Pearl on non-Markovian Models “... we confess our preparedness to miss the discovery of non-Markovian causal models that cannot be described as latent structures. I do not consider this loss to be very serious, because such models – even if any exist in the macroscopic world – would have limited utility as guides to decisions.” (2000, p. 62)

19. Pearl on Critics of the Markov Assumption “... they propose no alternative non-Markovian models from which one could predict the effects of actions and action combinations.” (2000, p. 62) Suggested Answer to this Challenge: Multi-Causal Forks + Sudbury's Theorems