Words, kinds and causal powers: A theory theory perspective on early naming and categorization. For some twenty-five years, the prevailing theories of categorization in philosophy have invoked the idea of “kinds ” (Putnam, 1975; Kripke, 1972). When we look at how adults use words to refer to categories of things we find that they only rarely categorize objects on the basis of their common properties. Instead, adults seem to categorize objects together when they believe that they belong to the same “kind”; that is, that they share some common, abstract “essence.” Psychological investigations of adults have largely confirmed these philosophical intuitions, adults do seem to group objects together based on “kinds ” rather than properties (Murphy &

Causal knowledge enables us to predict future events, to choose the right actions to achieve our goals, and to envision what would have happened if things had been different. Thus, it allows us to reason about observations, interventions and counterfactual possibilities.

Judea Pearl’s Causal Model is a rich framework that provides deep insight into the nature of causal relations. As yet, however, the Pearl Causal Model (PCM) has not had much impact on economics or econometrics. This may be due in part to the fact that the PCM is not as well suited to analyzing economic structures as might be desired. We o¤er the settable systems framework as an extension of the PCM that embodies features of central interest to economists and econometricians: optimization, equilibrium, and learning. Because these are common features of physical, natural, or social systems, our framework may prove generally useful. In particular, settable systems o¤er a number of advantages relative to the PCM for machine learning. Important distinguishing features of the settable systems framework are its countable dimensionality, its treatment of attributes, the absence of a …xed-point requirement, and the use of partitioning and partition-speci…c response functions to accommodate the behavior of optimizing and interacting agents. A series of closely related machine learning examples and examples from game theory and machine learning with feedback demonstrates limitations of the PCM and motivates the distinguishing features of settable systems.

Information Theory,

Using Directed Acyclic Graphs (DAG's) and Error Correction Models we study the dynamics of the notoriously volatile international freight prices that comprise the Baltic Panamax Index, the index on which freight futures trading is based. The DAG's are used to make definitive statements about the contemporaneous correlations between prices and allow us to address the construction of the data-determined orthoganization on contemporaneous innovation covariance, critical in providing sound inference in innovation accounting techniques. Our results provide a rich source of information on price discovery over various time horizons and suggest that the index may not be appropriately comprised and weighted.

On behalf of:

The investigation of probabilistic causality has been plagued by a variety of misconceptions and misunderstandings. One has been the thought that the aim of the probabilistic account of causality is the reduction of causal claims to probabilistic claims. Nancy Cartwright (1979) has clearly rebutted that idea. Another ill-conceived idea continues to haunt the debate, namely the idea that contextual unanimity can do the work of objective homogeneity. It cannot. We argue that only objective homogeneity in combination with a causal interpretation of Bayesian networks can provide the desired criterion of probabilistic causality.

Abstract This paper demonstrates the extensive scope of an alternative to standard instrumental variables methods, namely covariate-based methods, for identifying and estimating e¤ects of interest in general structural systems. As we show, commonly used econometric methods, speci…cally parametric, semi-parametric, and nonparametric extremum or moment-based methods, can all exploit covariates to estimate well-identi…ed structural e¤ects. The systems we consider are general, in that they need not impose linearity, separability, or monotonicity restrictions on the structural relations. We consider e¤ects of multiple causes; these may be binary, categorical, or continuous. For continuous causes, we examine both marginal and non-marginal e¤ects. We analyze e¤ects on aspects of the response distribution generally, de…ned by explicit or implicit moments or as optimizers (e.g., quantiles). Key for identi…cation is a speci…c conditional exogeneity relation. We examine what happens in its absence and …nd that identi…cation generally fails. Nevertheless, local and near identi…cation results hold in its absence, as we show.

this paper another misguided attempt to reduce causation to probability. But causation leaves a distinct probabilistic signature; here we are concerned with the probabilistic signatures left Thanks to Chris Wallace and Lucas Hope for discussions and comments. NSF grant SES 99-06565 supported Twardy during this work

Models of incomplete information rely on probabilistic signals to capture the uncertain values of variables allowing the relationship between signals and the relevant variables to be captured by a joint probability distribution function. In this paper, we de ne and contrast two types of signals: generated signals and interpreted signals. Generated signals are distortions of the true values or outputs of some process. Interpreted signals are predictions based on inputs of a process or attributes of an object. Binary discrete interpreted signals are negatively correlated in their correctness, be it conditional or unconditional; moreover, the amount of negative correlation is uniquely determined in important cases. In contrast, generated signals can be independent conditional on the true value. In other words, these two types of signals produce distinct statistical signatures. Thus, our ndings limit the contexts in which many well known models of information aggregation and strategic choices in auctions, markets, and voting apply.