The role of assumptions in causal discovery

The paper looks at the conditional independence search approach to causal discovery, proposed by Spirtes et al. and Pearl and Verma, from the point of view of the mechanism-based view of causality in econometrics, explicated by Simon. As demonstrated by Simon, the problem of determining the causal structure from data is severely underconstrained and the perceived causal structure depends on the a priori assumptions that one is willing to make. I discuss the assumptions made in the independence search-based causal discovery and their identifying strength

A comparison of popular fertility awareness methods to a DBN model of the woman's monthly cycle

Fertility Awareness Methods are effective, safe, and low-cost techniques for identifying the fertile days of a menstrual cycle. In this paper, we compare the effectiveness of predicting the fertile days by a Dynamic Bayesian Network model of the monthly cycle to 11 existing Fertility Awareness Methods. We base our comparison on a real data set of 7,017 cycles collected by 881 women. We demonstrate that the DBN model is more accurate than the best modern Fertility Awareness Methods, based on the observation of mucus, marking reasonably high percentage of days of the cycle as infertile. We argue that the DBN approach offers other advantages, such as predicting the ovulation day and being able to adjust its predictions to each woman's individual cycle