It is commonplace to encounter nonstationary data, of which the underlying generating process may change over time or across domains. The nonstationarity presents both challenges and opportunities for causal discovery. In this paper we propose a principled framework to handle nonstationarity, and develop some methods to address three important questions. First, we propose an enhanced constraint-based method to detect variables whose local mechanisms are nonstationary and recover the skeleton of the causal structure over observed variables. Second, we present a way to determine some causal directions by taking advantage of information carried by changing distributions. Third, we develop a method for visualizing the nonstationarity of causal modules. Experimental results on various synthetic and real-world data sets are presented to demonstrate the efficacy of our methods.

This introduction focuses on how human-centered computing (HCC) is changing the way that people think about information technology. The AI perspective views this HCC framework as embodying a systems view, in which human thought and action are linked and equally important in terms of analysis, design, and evaluation. This emerging technology provides a new research outlook for AI applications, with new research goals and agendas.

This paper (1)shows that the best supported current psychological theory (Cheng, 1997) of how human subjects judge the causal power or influence of variations in presence or absence of one feature on another, given data on their covariation, tacitly uses a Bayes network which is either a noisy or gate (for causes that promote the effect) or a noisy and gate (for causes that inhibit the effect); (2)generalizes Chengs theory to arbitrary acyclic networks of noisy or and noisy and gates; (3)gives various sufficient conditions for the estimation of the parameters in such networks when there are independent, unobserved causes; (4)distinguishes direct causal influence of one feature on another (influence along a path with one edge) from total influence (influence along all paths from one variable to another) and gives sufficient conditions for estimating each when there are unobserved causes of the outcome variable; (5)describes the relation between Cheng models and a simplified version of the Rubin framework for representing causal relations.

It is "well known" that in linear models: (1) testable constraints on the marginal distribution of observed variables distinguish certain cases in which an unobserved cause jointly influences several observed variables; (2) the technique of "instrumental variables" sometimes permits an estimation of the influence of one variable on another even when the association between the variables may be confounded by unobserved common causes; (3) the association (or conditional probability distribution of one variable given another) of two variables connected by a path or trek can be computed directly from the parameter values associated with each edge in the path or trek; (4) the association of two variables produced by multiple treks can be computed from the parameters associated with each trek; and (5) the independence of two variables conditional on a third implies the corresponding independence of the sums of the variables over all units conditional on the sums over all units of each of the original conditioning variables.These properties are exploited in search procedures. It is also known that properties (2)-(5) do not hold for all Bayes nets with binary variables. We show that (1) holds for all Bayes nets with binary variables and (5) holds for all singly trek-connected Bayes nets of that kind. We further show that all five properties hold for Bayes nets with any DAG and binary variables parameterized with noisy-or and noisy-and gates.

Observed associations in a database may be due in whole or part to variations in unrecorded (latent) variables. Identifying such variables and their causal relationships with one another is a principal goal in many scientific and practical domains. Previous work shows that, given a partition of observed variables such that members of a class share only a single latent common cause, standard search algorithms for causal Bayes nets can infer structural relations between latent variables. We introduce an algorithm for discovering such partitions when they exist. Uniquely among available procedures, the algorithm is (asymptotically) correct under standard assumptions in causal Bayes net search algorithms, requires no prior knowledge of the number of latent variables, and does not depend on the mathematical form of the relationships among the latent variables. We evaluate the algorithm on a variety of simulated data sets.

In many domains, data are distributed among datasets that share only some variables; otherrecorded variables may occur in only one dataset. While there are asymptotically correct, informative algorithms for discovering causal relationships froma single dataset, even with missing values and hidden variables, there have been no such reliable procedures for distributed data with overlapping variables. Wepresent a novel, asymptotically correct procedure that discovers a minimal equivalence class of causal DAG structures using local independence information fromdistributed data of this form and evaluate its performance using synthetic and real-world data against causal discovery algorithms for single datasets and applying Structural EM, a heuristic DAG structure learning procedure for data with missing values, to the concatenated data.

Lull's writings advanced the idea vert to Christianity because of a cognitive that non-mathematical reasoning can in artificial intelligence have defect. Some of appreciate the vast array of the combinations process, and that reasoning the most fundamental, surely, are that of God's or Christ's virtues. But does not proceed by syllogism, but by thinking is a computational process, Lull believed that infidels could be combinatorics. The decomposition can be made mechanical, and that the Further, he thought that a representation and recombination of attributes can be mathematics of computation involves of those combinations could represented by the decomposition and combinatorics. All of these ideas have be effectively presented by means of recombination of symbols, and that, as their origin, so far as we know, in the appropriate machines, and that was Lull's devices illustrate, is a process that work of an eccentric 13th century the key to his new method.

With a power screwdriver the computer, the web, robots, the Europe the Hindu-Arabic system of anyone can drive the hardest screw; automation of manufacturing will all numbers and the arithmetic algorithms with a calculator, anyone can get the conspire to separate the rich and they made possible. One of the numbers right; with an aircraft anyone quick from the poor and slow, hurrying first books after the Bible printed with can fly to Paris; and with Deep the trend to an informed, skilled, moveable type was an Arithmetic. Blue, anyone can beat the world chess and employed elite living among an Even so, the algorithms were not easy champion. Cognitive prostheses undermine uninformed, unskilled, and unemployed and not widely disseminated. But both history and 17th century tradesman could not by giving non-experts equivalent an understanding of human-machine multiply.