منابع مشابه
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 always optimal. In some cases it is impossible or it would be unethical to perform such an intervention. We provide an account of “hard” and “soft” interventions, and discuss what they can contribut...
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We study the problem of using causal models to improve the rate at which good interventions can be learned online in a stochastic environment. Our formalism combines multi-arm bandits and causal inference to model a novel type of bandit feedback that is not exploited by existing approaches. We propose a new algorithm that exploits the causal feedback and prove a bound on its simple regret that ...
متن کاملCausal inference methods to study nonrandomized, preexisting development interventions.
Empirical measurement of interventions to address significant global health and development problems is necessary to ensure that resources are applied appropriately. Such intervention programs are often deployed at the group or community level. The gold standard design to measure the effectiveness of community-level interventions is the community-randomized trial, but the conditions of these tr...
متن کاملCausal inference techniques for trials of complex interventions
A statistical framework based on potential outcomes has been developed in which causal effects can be unambiguously defined, and the assumptions needed for their estimation clearly stated. This has also led to the development of new statistical methods that are especially designed for making causal inferences from non-randomised exposures under transparent, less restrictive and more plausible a...
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ژورنال
عنوان ژورنال: Philosophy of Science
سال: 2007
ISSN: 0031-8248,1539-767X
DOI: 10.1086/525638