Counterfactuals and double prevention: Trouble for the Causal Independence thesis

2020 ◽  
Vol 9 (3) ◽  
pp. 198-206
Author(s):  
David Turon
Keyword(s):  
Causal Independence

Causal independence of free quantum fields

1979 ◽  
Vol 29 (1) ◽  
pp. 29-32 ◽  
Author(s):  
S. S. Horujy ◽  
K. Yu. Dadashyan
Keyword(s):  
Quantum Fields ◽  
Causal Independence

scholarly journals INFORMATION FLOWS IN CAUSAL NETWORKS

2008 ◽  
Vol 11 (01) ◽  
pp. 17-41 ◽  
Author(s):  
NIHAT AY ◽  
DANIEL POLANI
Keyword(s):  
Information Flow ◽  
Causal Effect ◽  
Fundamental Concept ◽  
Transfer Entropy ◽  
Information Flows ◽  
Causal Independence ◽  
Causal Networks

We use a notion of causal independence based on intervention, which is a fundamental concept of the theory of causal networks, to define a measure for the strength of a causal effect. We call this measure "information flow" and compare it with known information flow measures such as transfer entropy.

scholarly journals Exploring the role of stimulus similarity on the summation effect in causal learning

2017 ◽  
Author(s):  
Omar D. Pérez ◽  
Rene San Martín ◽  
Fabián A. Soto
Keyword(s):  
Associative Learning ◽  
Visual Cues ◽  
Causal Learning ◽  
Spatial Position ◽  
Stimulus Similarity ◽  
Summation Effect ◽  
Causal Independence

AbstractSeveral contemporary models of associative learning anticipate that the higher responding to a compound of two cues separately trained with a common outcome than to each of the cues alone -a summation effect-is modulated by the similarity between the cues forming the compound. Here, we explored this hypothesis in a series of causal learning experiments with humans. Participants were presented with two visual cues that separately predicted a common outcome and later asked for the outcome predicted by the compound of the two cues. Importantly, the cues’ similarity was varied between groups through changes in shape, spatial position, color, configuration and rotation. In variance with the predictions of these models, we observed similar and strong levels of summation in both groups across all manipulations of similarity (Experiments 1-5). The summation effect was significantly reduced by manipulations intended to impact assumptions about the causal independence of the cues forming the compound, but this reduction was independent of stimulus similarity (Experiment 6). These results are problematic for similarity-based models and can be more readily explained by rational approaches to causal learning.

scholarly journals Probabilistic Event Structures and Domains

2004 ◽  
Vol 11 (10) ◽  
Author(s):  
Daniele Varacca ◽  
Hagen Völzer ◽  
Glynn Winskel
Keyword(s):  
Representation Theorem ◽  
Event Structure ◽  
Domain Theory ◽  
Probabilistic Choice ◽  
Event Structures ◽  
Causal Independence ◽  
Probabilistic Processes

This paper studies how to adjoin probability to event structures, leading to the model of probabilistic event structures. In their simplest form probabilistic choice is localised to cells, where conflict arises; in which case probabilistic independence coincides with causal independence. An application to the semantics of a probabilistic CCS is sketched. An event structure is associated with a domain--that of its configurations ordered by inclusion. In domain theory probabilistic processes are denoted by continuous valuations on a domain. A key result of this paper is a representation theorem showing how continuous valuations on the domain of a confusion-free event structure correspond to the probabilistic event structures it supports. We explore how to extend probability to event structures which are not confusion-free via two notions of probabilistic runs of a general event structure. Finally, we show how probabilistic correlation and probabilistic event structures with confusion can arise from event structures which are originally confusion-free by using morphisms to rename and hide events.

Morgenbesser’s Coin

2021 ◽  
Vol 51 (5) ◽  
pp. 317-328
Author(s):  
Yael Loewenstein
Keyword(s):  
Compelling Reason ◽  
Causal Independence ◽  
Coin Tossing ◽  
Reductive Analysis

AbstractBefore a fair, indeterministic coin is tossed, Lucky, who is causally isolated from the coin-tossing mechanism, declines to bet on heads. The coin lands heads. The consensus is that the following counterfactual is true:(M:) If Lucky had bet heads, he would have won the bet.It is also widely believed that to rule (M) true, any plausible semantics for counterfactuals must invoke causal independence. But if that’s so, the hope of giving a reductive analysis of causation in terms of counterfactuals is undermined. Here I argue that there is compelling reason to question the assumption that (M) is true.

scholarly journals Learning symmetric causal independence models

2008 ◽  
Vol 71 (2-3) ◽  
pp. 133-153 ◽  
Author(s):  
Rasa Jurgelenaite ◽  
Tom Heskes
Keyword(s):  
Causal Independence
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