Bayesian nets are all there is to causal dependence

Quality and Quantity, 2008

We analyse the concept of causality in the social sciences, whose development is insufficient and lesser than the methodology developed for its study. The nature of the causal process as the production of effects remains unclear and the relationships considered to be manifestations of that process cannot be taken for proof of its existence. Given these difficulties, we suggest that, aside from the inherited interpretations, the practice of the concept of causality makes reference to correctly specified relationships not confounded by others; characteristics identical to those which define validity. In that way, causality is equivalent to the validity of a relationship. Beyond merely re-understanding causality, this proposal permits the deduction that the temporal precedence of the cause is a necessary condition only for one type of causality, making it possible to consider other types, not admitted by the traditional notion, in which the cause is consequent or simultaneous to the variable to be explained. Examples and characteristics of these types of causality are presented and considered to be useful for the social sciences.

Erkenntnis, 2008

This paper addresses a problem that arises when it comes to inferring deterministic causal chains from pertinent empirical data. It will be shown that to every deterministic chain there exists an empirically equivalent common cause structure. Thus, our overall conviction that deterministic chains are one of the most ubiquitous (macroscopic) causal structures is underdetermined by empirical data. It will be

We start this paper by arguing that causality should, in analogy with force in Newtonian physics, be understood as a theoretical concept that is not explicated by a single definition, but by the axioms of a theory. Such an understanding of causality implicitly underlies the well-known theory of causal (Bayes) nets (TCN) and has been explicitly promoted by Glymour (Br J Philos Sci 55: [779][780][781][782][783][784][785][786][787][788][789][790] 2004). In this paper we investigate the explanatory warrant and empirical content of TCN. We sketch how the assumption of directed cause-effect relations can be philosophically justified by an inference to the best explanation. We then ask whether the explanations provided by TCN are merely post-facto or have independently testable empirical content. To answer this question we develop a fine-grained axiomatization of TCN, including a distinction of different kinds of faithfulness. A number of theorems show that although the core axioms of TCN are empirically empty, extended versions of TCN have successively increasing empirical content.

Journal of Machine Learning Research

The goal of many sciences is to understand the mechanisms by which variables came to take on the values they have (that is, to find a generative model), and to predict what the values of those variables would be if the naturally occurring mechanisms were subject to outside manipulations. The past 30 years has seen a number of conceptual developments that are partial solutions to the problem of causal inference from observational sample data or a mixture of observational sample and experimental data, particularly in the area of graphical causal modeling. However, in many domains, problems such as the large numbers of variables, small samples sizes, and possible presence of unmeasured causes, remain serious impediments to practical applications of these developments. The articles in the Special Topic on Causality address these and other problems in applying graphical causal modeling algorithms. This introduction to the Special Topic on Causality provides a brief introduction to graphical causal modeling, places the articles in a broader context, and describes the differences between causal inference and ordinary machine learning classification and prediction problems.

Uncertainty Proceedings 1994, 1994

Most traditional models of uncertainty have focused on the associational relationship among variables as captured by conditional dependence. In order to successfully manage intelligent systems for decision making, however, we must be able to predict the effects of actions. In this paper, we attempt to unite two branches of research that address such predictions: causal modeling and decision analysis. First, we provide a definition of causal dependence in decision-analytic terms, which we derive from consequences of causal dependence cited in the literature. Using this definition, we show how causal dependence can be represented within an influence diagram. In particular, we identify two inadequacies of an ordinary influence diagram as a representation for cause. We introduce a special class of influence diagrams, called causal influence diagrams, which corrects one of these problems, and identify situations where the other inadequacy can be eliminated. In addition, we describe the relationships between Howard Canonical Form and existing graphical representations of cause.

2000

2000

Handbooks of Sociology and Social Research, 2013

This chapter discusses the use of directed acyclic graphs (DAGs) for causal inference in the observational social sciences. It focuses on DAGs' main uses, discusses central principles, and gives applied examples. DAGs are visual representations of qualitative causal assumptions: They encode researchers' beliefs about how the world works. Straightforward rules map these causal assumptions onto the associations and independencies in observable data. The two primary uses of DAGs are (1) determining the identifiability of causal effects from observed data and (2) deriving the testable implications of a causal model. Concepts covered in this chapter include identification, d-separation, confounding, endogenous selection, and overcontrol. Illustrative applications then demonstrate that conditioning on variables at any stage in a causal process can induce as well as remove bias, that confounding is a fundamentally causal rather than an associational concept, that conventional approaches to causal mediation analysis are often biased, and that causal inference in social networks inherently faces endogenous selection bias. The chapter discusses several graphical criteria for the identification of causal effects of single, time-point treatments (including the famous backdoor criterion), as well identification criteria for multiple, time-varying treatments.

Statistica, 2010