Causal effect evaluation and causal network learning are two main research areas in causal inference. For causal effect evaluation, we review the two problems of confounders and surrogates. The Yule-Simpson paradox is the idea that the association between two variables may be changed...

There has been considerable attention on estimation of conditional variance function in the literature. We propose here a nonparametric model for conditional covariance matrix. A kernel estimator is developed accordingly, its asymptotic bias and variance are derived, and its asymptotic normality...

Given a target variable and observational data, we propose a sequential learning approach for discovering direct cause and effect variables of the target under the causal network framework. In the approach, we start from the target, sequentially find Markov blankets of variables and learn local...

type="main" xml:id="rssb12032-abs-0001" <title type="main">Summary</title> <p>Discovering patterns from a set of text or, more generally, categorical data is an important problem in many disciplines such as biomedical research, linguistics, artificial intelligence and sociology. We consider here the well-known ‘market...</p>

We derive an explicit form of a Markov basis on the junction tree for a decomposable log-linear model. Then we give a description of a Markov basis characterized by global Markov properties associated with the graph of a decomposable log-linear model and show how to use the Markov basis for...

Evaluating the causal effect of an exposure on a response from case-control and cohort studies is a major concern in epidemiological and medical research. Since causal effects are in general nonidentifiable from such studies, this paper derives bounds on two causal measures: the causal risk...