Dawn E. Gregory and Paul R. Cohen
Many methods have been developed for inducing cause from statistical data. Those employing linear regression have historically been discounted, due to their inability to distinguish true from spurious cause. We present a regression-based statistic that avoids this problem by separating direct and indirect influences. We use this statistic in two causal induction algorithms, each taki=g a different approach to constructing causal models. We demonstrate empirically the accuracy of these algorithms.