Structural Equation Models (SEM) is one of the most important tools for causal analysis in the social and behavioral sciences (e.g., Economics, Sociology, etc). A central problem in the application of SEM models is the analysis of Identification. Succintly, a model is identified if it only admits a unique parametrization to be compatible with a given covariance matrix (i.e., observed data). The identification of a model is important because, in general, no reliable quantitative conclusion can be derived from non-identified models.

In this work, we develop a new approach for the analysis of identification in SEM, based on graph theoretic techniques. Our main result is a general sufficient criterion for model identification. The criterion consists of a number of graphical conditions on the causal diagram of the model. We also develop a new method for computing correlation constraints imposed by the structural assumptions, that can be used for model testing. Finally, we also provide a generalization to the traditional method of Instrumental Variables, through the concept of Instrumental Sets.

The paper concerns the problem of predicting the e�ffect of actions or interventions on a system from a combination of (i) statistical data on a set of observed variables, and (ii) qualitative causal knowledge encoded in the form of a directed acyclic graph (DAG). The DAG represents a set of linear equations called Structural Equations Model (SEM), whose coefficients are parameters representing direct causal effects. Reliable quantitative conclusions can only be obtained from the model if the causal effects are uniquely determined by the data. That is, if there exists a unique parameterization for the model that makes it compatible with the data. If this is the case, the model is called identifi�ed. The main result of the paper is a general sufficient condition for identifi�cation of recursive SEM models.

This Article examines the widespread phenomenon of exorbitant child support debt owed by noncustodial fathers in no- and low-income and predominately Black families. Drawing from qualitative data—including a court-based ethnography and in-depth interviews with lawyers, litigants, and judges—this Article explores the inflated and arbitrary nature of the debt, detailing how states utilize family law rules, child support system practices, and court processes to construct burdensome child support arrears that many poor noncustodial fathers will never have the means to pay off. It concludes by arguing that inflated child support arrears should be questioned and challenged.

This paper concerns the assessment of direct causal effects from a combination of: (i) non-experimental data, and (ii) qualitative domain knowledge. Domain knowledge is encoded in the form of a directed acyclic graph (DAG), in which all interactions are assumed linear, and some variables are presumed to be unobserved. We provide a generalization of the well-known method of Instrumental Variables, which makes allows its application to models with few conditional independencies.