搜索结果: 1-12 共查到“知识库 causal inference”相关记录12条 . 查询时间(0.043 秒)
Causal inference,mechanisms,and the Semmelweis case
methodology causal inference numerical tables mechanisms animal experiments
2016/6/15
Semmelweis's discovery of the cause of puerperal fever around the middle of the 19th century counts among the paradigm cases of scientific discovery. For several decades, philosophers of science have ...
An Epistemology of Causal Inference from Experiment
causation experiment Gasparo Berti water barometer vacuum
2016/5/31
The manipulationist account of causation provides a conceptual analysis of cause-effect relationships in terms of hypothetical experiments. It also explains why and how experiments are used for the em...
The linguistic description of minimal social scenarios affects the extent of causal inference making
Social scenario Event Scenario Verb Inference
2015/3/31
There is little consensus regarding the circumstances in which people spontaneously generate causal inferences, and in particular whether they generate inferences about the causal antecedents or the c...
Generalized Thompson Sampling for Sequential Decision-Making and Causal Inference
Generalized Thompson Sampling Sequential Decision-Making Causal Inference
2013/5/2
Recently, it has been shown how sampling actions from the predictive distribution over the optimal action-sometimes called Thompson sampling-can be applied to solve sequential adaptive control problem...
Geometry of faithfulness assumption in causal inference
causal inference PC-algorithm (strong) faithfulness conditional independence directed acyclic graph structural equation model real algebraic hypersurface Crofton's formula algebraic statistics.
2012/9/18
Many algorithms for inferring causality rely heavily on the faithfulness assumption.The main justi cation for imposing this assumption is that the set of unfaithful distribu-tions has Lebesgue measure...
Causal Inference on Time Series using Structural Equation Models
Causal Inference Time Series Structural Equation Models
2012/9/19
Causal inference uses observations to infer the causal structure of the data generating system.We study a class of functional models that we call Time Series Models with Independent Noise (TiMINo). Th...
A practical illustration of the importance of realistic individualized treatment rules in causal inference
Experimental Treatment Assignment assumption positivity assumption dynamic treatment rules physical activity
2009/9/16
The effect of vigorous physical activity on mortality in the elderly is difficult to estimate using conventional approaches to causal inference that define this effect by comparing the mortality risks...
Causal inference in longitudinal studies with history-restricted marginal structural models
causal inference counterfactual marginal structural model longitudinal study IPTW G-computation Double Robust
2009/9/16
A new class of Marginal Structural Models (MSMs), History-Restricted MSMs (HRMSMs), was recently introduced for longitudinal data for the purpose of defining causal parameters which may often be bette...
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 a...
Detection of Unfaithfulness and Robust Causal Inference
Robust Causal Detection of Unfaithfulness
2008/4/11
Many algorithms proposed in the machine learning community for inferring causality from data are grounded on two assumptions, known as the Causal Markov Condition and the Causal Faithfulness Condition...
Causal Inference in Quantum Mechanics: A Reassessment
quantum mechanics EPR correlations principle of common cause
2008/4/10
There has been an intense discussion, albeit largely an implicit one, concerning the inference of causal hypotheses from statistical correlations in quantum mechanics ever since John Bell’s first stat...
Causal inference in longitudinal studies with history-restricted marginal structural models
causal inference counterfactual marginal structuralmodel longitudinal study IPTW G-computation Double Robust
2010/4/29
A new class of Marginal Structural Models (MSMs), History-
Restricted MSMs (HRMSMs), was recently introduced for longitudinal data
for the purpose of defining causal parameters which may often be be...