Indeterminism and the Causal Markov Condition
نویسنده
چکیده
The causal Markov condition (CMC) plays an important role in much recent work on the problem of causal inference from statistical data. It is commonly thought that the CMC is a more problematic assumption for genuinely indeterministic systems than for deterministic ones. In this essay, I critically examine this proposition. I show how the usual motivation for the CMC—that it is true of any acyclic, deterministic causal system in which the exogenous variables are independent—can be extended to the indeterministic case. In light of this result, I consider several arguments for supposing indeterminism a particularly hostile environment for the CMC, but conclude that none are persuasive.
منابع مشابه
Is determinism more favorable than indeterminism for the causal Markov condition?
The present text comments on a paper by Daniel Steel, in which the author claims to extent from the deterministic to the general case the result according to which the causal Markov condition is satis ed by systems with jointly independent exogenous variables. I show that Steel's claim cannot be accepted unless one is prepared to abandon standard causal modeling terminology. Correlatively, I ar...
متن کاملA Transitivity Heuristic of Probabilistic Causal Reasoning
In deterministic causal chains the relations „A causes B’ and „B causes C’ imply that „A causes C’. However, this is not necessarily the case for probabilistic causal relationships: A may probabilistically cause B, and B may probabilistically cause C, but A does not probabilistically cause C, but rather ¬C. The normal transitive inference is only valid when the Markov condition holds, a key fea...
متن کاملCausal Assumptions: Some Responses To
ii ACKNOWLEDGMENTS iii LIST OF FIGURES v INTRODUCTION 1 NOTATION AND TERMINOLOGY, DEFINITIONS, ASSUMPTIONS, AND ALGORITHMS 3 DAGS and Probability Distributions 3 Causal Graphs 9 The Markov Condition and the Causal Markov Condition 21 CARTWRIGHT’S CRITIQUE 22 The Faithfulness Assumption 22 The Markov Condition and the Factory Example 24 SOME RESPONSES TO CARTWRIGHT 29 The Factory Example and the...
متن کاملFrom Metaphysics to Method: Comments on Manipulability and the Causal Markov Condition
Daniel Hausman and James Woodward claim to prove that the causal Markov condition, so important to Bayes-nets methods for causal inference, is the ‘flip side’ of an important metaphysical fact about causation—that causes can be used to manipulate their effects. This paper disagrees. First, the premise of their proof does not demand that causes can be used to manipulate their effects but rather ...
متن کاملManipulation and the Causal Markov Condition
This paper explores the relationship between a manipulability conception of causation and the causal Markov condition (CM). We argue that violations of CM also violate widely shared expectations—implicit in the manipulability conception—having to do with the absence of spontaneous correlations. They also violate expectations concerning the connection between independence or dependence relations...
متن کامل