Causal Discovery from a Mixture of Experimental and Observational Data

因果知识构成了我们在科学中所知道和想知道的大部分内容。这个目标 研究是发展和应用一种统一的方法来表示和发现因果关系 从观测和实验数据的混合物。概率因果网络被用作 因果关系的表现。贝叶斯方法正在被应用于学习概率因果网络 from data数据.发现方法的计算机实现正在进行实证研究，使用 从人类专家构建的现有因果模型生成的数据。能够使用 观察和实验数据的混合将大大扩展贝叶斯因果建模和科学发现的应用范围。 http://www.cbmi.upmc.edu/~gfc/index.html/causal_discovery

Causal knowledge makes up much of what we know and want to know in science. The goal of this research is to develop and apply a unified method for representing and discovering causal relationships from a mixture of observational and experimental data. Probabilistic causal networks are being used as a representation of causality. Bayesian methods are being applied to learn probabilistic causal networks from data. A computer implementation of the discovery method is being investigated empirically using data generated from existing causal models that were constructed by human experts. The ability to use a mixture of observational and experimental data will expand considerably the scope of application of Bayesian causal modeling and discovery in science. http://www.cbmi.upmc.edu/~gfc/index.html/causal_discovery