A data-driven approximate causal inference model using the evidential reasoning rule

2015 ◽  
Vol 88 ◽  
pp. 264-272 ◽  
Author(s):  
Yue Chen ◽  
Yu-Wang Chen ◽  
Xiao-Bin Xu ◽  
Chang-Chun Pan ◽  
Jian-Bo Yang ◽  
...  
Keyword(s):  
Causal Inference ◽  
Evidential Reasoning ◽  
Data Driven ◽  
Inference Model

scholarly journals Impact of echinocandin on prognosis of proven invasive candidiasis in ICU: A post-hoc causal inference model using the AmarCAND2 study

2017 ◽  
Vol 74 (4) ◽  
pp. 408-417 ◽  
Author(s):  
Sébastien Bailly ◽  
Olivier Leroy ◽  
Elie Azoulay ◽  
Philippe Montravers ◽  
Jean-Michel Constantin ◽  
...  
Keyword(s):  
Causal Inference ◽  
Invasive Candidiasis ◽  
Inference Model ◽  
Post Hoc

scholarly journals Tools for causal inference from cross-sectional innovation surveys with continuous or discrete variables: Theory and applications

2018 ◽  
Vol 37 (75) ◽  
pp. 779-808 ◽  
Author(s):  
Alex Coad ◽  
Dominik Janzing ◽  
Paul Nightingale
Keyword(s):  
Causal Inference ◽  
Learning Community ◽  
Firm Growth ◽  
Additive Noise ◽  
Public Funding ◽  
Data Driven ◽  
Cross Sectional ◽  
Innovation Surveys ◽  
Noise Models ◽  
D Investment

This paper presents a new statistical toolkit by applying three techniques for data-driven causal inference from the machine learning community that are little-known among economists and innovation scholars: a conditional independence-based approach, additive noise models, and non-algorithmic inference by hand. We include three applications to CIS data to investigate public funding schemes for R&D investment, information sources for innovation, and innovation expenditures and firm growth. Preliminary results provide causal interpretations of some previously-observed correlations. Our statistical 'toolkit' could be a useful complement to existing techniques.

Towards a Neural Implementation of Causal Inference in Cue Combination

2013 ◽  
Vol 26 (1-2) ◽  
pp. 159-176 ◽  
Author(s):  
Wei Ji Ma ◽  
Masih Rahmati
Keyword(s):  
Causal Inference ◽  
Population Coding ◽  
Sensory Cues ◽  
Cue Combination ◽  
Inference Model ◽  
Sensory Cue

Causal inference in sensory cue combination is the process of determining whether multiple sensory cues have the same cause or different causes. Psychophysical evidence indicates that humans closely follow the predictions of a Bayesian causal inference model. Here, we explore how Bayesian causal inference could be implemented using probabilistic population coding and plausible neural operations, but conclude that the resulting architecture is unrealistic.

scholarly journals A causal inference model for the perception of complex motion in the presence of self-motion

2020 ◽  
Vol 20 (11) ◽  
pp. 1631
Author(s):  
Sabyasachi Shivkumar ◽  
Gregory C. DeAngelis ◽  
Ralf M. Haefner
Keyword(s):  
Causal Inference ◽  
Complex Motion ◽  
Inference Model ◽  
Self Motion
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