It does seem to me worth noting that if P is a probability distribution, and if for any A and B, P B (A) = P(B >A), then P B is a probability distribution too (excepting the absurd case). What it is good for, I would like to suggest, is deliberation — the calculation of expected utilities.

Preface to the second edition Preface to the original edition Acknowledgments 1. Sex and justice 2. Fairness and commitment 3. Mutual aid 4. Correlated convention 5. The evolution of meaning Postscript Notes References Index.

Letter to David Lewis

Evolution of the social contract

: The role of causality in SEM research is widely perceived to be, on the one hand, of pivotal methodological importance and, on the other hand, confusing, enigmatic and controversial. The confusion is vividly portrayed, for example, in the influential report of Wilkinson and Task Force s (1999) on Statistical Methods in Psychology Journals: Guidelines and Explanations. In discussing SEM, the report starts with the usual warning: It is sometimes thought that correlation does not prove causation but causal modeling does. [Wrong! There are] dangers in this practice. But then ends with a startling conclusion: The use of complicated causal-modeling software [read SEM] rarely yields any results that have any interpretation as causal effects. The implication being that the entire enterprise of causal modeling, from Sewell Wright (1921) to Blalock (1964) and Duncan (1975), the entire literature in econometric research, including modern advances in graphical and nonparametric structural models have all been misguided, for they have been chasing parameters that have no causal interpretation.

/pdf/the-causal-foundations-of-structural-equation-modeling-5dj6xctokr.pdf

The Causal Foundations of Structural Equation Modeling

Belief revisions and the Ramsey test for conditionals

Remove the abstract (and move to the Main Text). In two recent communications, Cole and Frangakis and VanderWeele conclude that the consistency rule used in causal inference is an assumption that precludes any side-effects of treatment/exposure on the outcomes of interest. They further develop auxiliary notation to make this assumption formal and explicit. I argue that the consistency rule is a theorem in the logic of counterfactuals and need not be altered, even in cases where different versions of treatment/exposure have side effects on the outcome. Instead, warnings of potential side-effects should be embodied in standard modeling practices, using graphs or structural equation models, in which causal assumptions are given transparent and unambiguous representation. In two recent communications, Cole and Frangakis 1 and VanderWeele 2 interpret the consistency rule of causal inference an assumption that is violated whenever versions of treatment/exposure have unintended side effects on the outcomes of interest. They further develop auxiliary notation to make this assumption formal and explicit. I argue that the consistency rule is in fact a theorem in the logic of counterfactuals and need not be altered. Side-effects can be made explicit in standard modeling practices, whenever transparent models are used. Informally, the consistency rule states that a person’s potential outcome under a hypothetical condition that happened to materialize is precisely the outcome experienced by that person. When expressed formally, this rule reads 3 :

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On the Consistency Rule in Causal Inference: Axiom, Definition, Assumption, or Theorem?

We consider the dispute between causal decision theorists and evidential decision theorists over Newcomb-like problems. We introduce a framework relating causation and directed graphs developed by Spirtes et al. (1993) and evaluate several arguments in this context. We argue that much of the debate between the two camps is misplaced; the disputes turn on the distinction between conditioning on an event E as against conditioning on an event I which is an action to bring about E. We give the essential machinery for calculating the effect of an intervention and consider recent work which extends the basic account given here to the case where causal knowledge is incomplete. 1 The Markov condition 2 Intervening and conditioning 3 Inquiry and decision

/pdf/conditioning-and-intervening-2sd9ba2kpw.pdf

Letter to David Lewis

Citations

Evolution of the social contract

The Causal Foundations of Structural Equation Modeling

Belief revisions and the Ramsey test for conditionals

On the Consistency Rule in Causal Inference: Axiom, Definition, Assumption, or Theorem?

Conditioning and intervening

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