• Corpus ID: 198902336

Info Intervention

@article{Heyang2019InfoI,
  title={Info Intervention},
  author={Gong Heyang and Zhu Ke},
  journal={ArXiv},
  year={2019},
  volume={abs/1907.11090}
}
Pearl asserts that all three main obstacles for strong AI can be overcome using causal modeling tools, in particular, causal diagrams and their associated logic. We point out issues of existing definition of Pearl's do intervention (also known as "surgical" or "atomatic" or "perfect" intervention) and other notations for causation. To highlight the view of causality as information transfer we propose the info intervention, which intervening the sending out information. It turns out that info… 

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References

SHOWING 1-10 OF 41 REFERENCES
Pearl's Calculus of Intervention Is Complete
TLDR
It is proved that the three basic do-calculus rules that Pearl presents are complete, in the sense that, if a causal effect is identifiable, there exists a sequence of applications of the rules of the do-Calculus that transforms the causal effect formula into a formula that only includes observational quantities.
Concerning the consistency assumption in causal inference.
TLDR
A refinement of the consistency assumption is proposed that makes clear that the consistency statement is in fact an assumption and not an axiom or definition, and sheds light on the distinction between intervention and choice in reasoning about causality.
Causal inference by using invariant prediction: identification and confidence intervals
TLDR
This work proposes to exploit invariance of a prediction under a causal model for causal inference: given different experimental settings (e.g. various interventions) the authors collect all models that do show invariance in their predictive accuracy across settings and interventions, and yields valid confidence intervals for the causal relationships in quite general scenarios.
Comment: Graphical Models, Causality and Intervention
TLDR
I will focus on the connection between graphical models and the notion of causality in statistical analysis, and supplement the discussion with an ac'count of how causal models and graphical models are related.
Theoretical Aspects of Cyclic Structural Causal Models
Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounders
TLDR
The closedness of {\sigma}-separation under marginalisation and conditioning is proved and this leads to the first causal discovery algorithm that can handle non-linear functional relations, latent confounders, cyclic causal relationships, and data from different (stochastic) perfect interventions.
Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Bias
We prove the main rules of causal calculus (also called do-calculus) for i/o structural causal models (ioSCMs), a generalization of a recently proposed general class of non-/linear structural causal
Interventions and Causal Inference
The literature on causal discovery has focused on interventions that involve randomly assigning values to a single variable. But such a randomized intervention is not the only possibility, nor is it
Identifying the consequences of dynamic treatment strategies: A decision-theoretic overview
TLDR
It is shown that formulating the problem of assessing dynamic treatment strategies as a problem of decision analysis brings clarity, simplicity and generality.
A Second Chance to Get Causal Inference Right: A Classification of Data Science Tasks
TLDR
It is argued that a failure to adequately describe the role of subject-matter expert knowledge in data analysis is a source of widespread misunderstandings about data science and how to guide decision-making in the real world and to train data scientists.
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