Introduction: Characterizing the Questions of causal reasoning This chapter describes the mechanism approach to the study of causal reasoning. We will first offer a characterization of the central issues in human causal reasoning, and will discuss how the mechanism approach addresses these issues. In the course of this presentation, we will frequently compare the mechanism approach with alternative accounts based on analyses of covariation, or what is often termed the regularity view. The aims of this chapter are the following: to explain why covariation and mechanism are different, to discuss why such a distinction is actually a useful tool for our understanding of causal reasoning, and to explicate the complementary nature of the two views. Before presenting these two approaches, it is necessary first to offer a description of the domain or problem itself : namely, what are these alternative approaches to? Although there are a number of different ways of characterizing the study of

One of the most enduring controversies in drug research and policy concerns the extent to which early cannabis use may predispose to the use of other ‘‘hard’ ’ drugs such as cocaine and heroin (Kleiman, 1992; MacCoun, 1998). Stage theory posits that there is an invariant sequence in drug use with onset of use falling along a continuum with the licit drugs (tobacco, alcohol) being used before cannabis, which in turn precedes the use of other drugs such as cocaine and heroin (Kandel, 1975; Kandel and Faust, 1975). While such a sequence has been observed in numerous studies

This paper reviews recent advances in the foundations of causal inference and introduces a systematic methodology for defining, estimating and testing causal claims in experimental and observational studies. It is based on non-parametric structural equation models (SEM) – a natural generalization of those used by econometricians and social scientists in the 1950-60s, and provides a coherent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring the effects of potential interventions (also called “causal effects” or “policy evaluation”), as well as direct and indirect effects (also known as “mediation”), in both linear and non-linear systems. Finally, the paper clarifies the role of propensity score matching in causal analysis, defines the relationships between the structural and

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1997 Association for Child Psychology & Psychiatry.

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Mulaik, Johannes Textor, and other researchers from SEMNET for their comments on and critiques of our paper. Bollen’s work was partially supported by NSF SES 0617276. 1 EIGHT MYTHS ABOUT CAUSALITY AND STRUCTURAL EQUATION MODELS Social scientists ’ interest in causal effects is as old as the social sciences. Attention to the philosophical underpinnings and the methodological challenges of analyzing causality has waxed and waned. Other authors in this volume trace the history of the concept of causality in the social sciences and we leave this task to their skilled hands. But we do note that we are at a time when there is a renaissance, if not a revolution in the methodology of causal inference, and structural equation models play a major role in this renaissance. Our emphasis in this chapter is on causality and structural equation models (SEMs). If nothing else, the pervasiveness of SEMs justifies such a focus. SEM applications are published in numerous substantive journals. Methodological developments on SEMs regularly appear in journals such as Sociological Methods & Research, Psychometrika, Sociological Methodology, Multivariate Behavioral Research, Psychological Methods,

This paper reviews recent advances in the foundations of causal inference and introduces a systematic methodology for defining, estimating and testing causal claims in experimental and observational studies. It is based on non-parametric structural equation models (SEM)– a natural generalization of those used by econometricians and social scientists in the 1950-60s, and provides a coherent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring the effects of potential interventions (also called “causal effects ” or “policy evaluation”), as well as direct and indirect effects (also known as “mediation”), in both linear and non-linear systems. Finally, the paper clarifies the role of propensity score matching in causal analysis, defines the relationships between the structural and potential-outcome frameworks, and develops symbiotic tools that use the strong features of both.