An overview of relations among causal modelling methods

Chemometrics and Intelligent Laboratory Systems, 2001

A general methodology that carries out causal and path modelling by the same tools as known by linear regression is Ž. Ž. presented. Data can be one block like in PCA , two blocks like in regression analysis , several blocks, e.g., derived from multi-way data, or a network of data blocks. Causality questions that we typically ask in PCA can be carried out for each block of data. The data blocks can make up a path, where each node contains two adjoining blocks. The two neighbouring data blocks have either the same number of variables or the same number of samples. The methods are based on the H-principle of mathematical modelling of data. A very general path or network of data blocks can be analysed. An important aspect of this approach is that most methods of linear regression analysis can be carried out within this framework. The procedures Ž are based on projections of one latent structure onto the following one. These methods can therefore be used to detect dif-. Ž. ferential changes in the latent structure e.g., in loadings or scores from one block to another.

International journal of agriculture extension and social development, 2024

Path analysis is a form of multiple regression statistical analysis that is used to evaluate causal models by examining the relationships between a dependent variable and two or more independent variables. It was developed by Geneticist Sewell Wright in the year 1921 and he describes path analysis as a technique based on a series of multiple regressions analysis with the added assumption of a causal relationship. Path analysis and regression analysis have similarities as path analysis is essentially the multiple regression analysis with standardized variables and the ß coefficient in regression analysis is equivalent to the test of significance of path coefficients. Path analysis was first developed as a method to decompose correlation coefficient into different components. Path analysis is mainly composed of five elements namely exogenous variables, endogenous variables, path diagram, path coefficient and effects. Path analysis assumes that there is linear relationship among the variables and all the variables are measured in interval scale. Though the methodology was used by a geneticist at the beginning, later Blalock introduced this concept into social scientific research. The algebra and tracing rules have been simplified in path analysis technique compared to conventional statistical methods so that even people with very little statistical training could perform path analysis.

We propose a novel interpretation in classical path analysis, whereby the influence of k independent variables on a dependent variable can be analyzed. The approach should be useful to study a causal structure with the assumption that this structure is true for the situation investigated. We propose a new coefficient, Qi, which provides a better interpretation of classical path analysis. We provide an example in which effects of certain soil properties on grain yield of winter rye (Secale cereale L.) were examined.

Causal modeling generally involves the construction and use of diagrammatic representations of the causal assumptions, expressed as directed acyclic graphs (DAGs). Do such graphs have cognitive benefits, for example by facilitating user inferences involving the underlying causal models? In two empirical studies, participants were given a set of causal assumptions, then attempted to identify all the causal pathways linking two variables in the model implied by these causal assumptions. Participants who were provided with a path diagram expressing the assumptions were more successful at identifying indirect pathways than those given the assumptions in the form of lists. Furthermore, the spatial orientation of the causal flow in the graphical model (left to right or right to left) had effects on the speed and accuracy with which participants made these inferences.

arXiv: Methodology, 2020

Motivated by a recent series of diametrically opposed articles on the relative value of statistical methods for the analysis of path diagrams in the social sciences, we discuss from a primarily theoretical perspective selected fundamental aspects of path modeling and analysis based on a common re reflexive setting. Since there is a paucity of technical support evident in the debate, our aim is to connect it to mainline statistics literature and to address selected foundational issues that may help move the discourse. We do not intend to advocate for or against a particular method or analysis philosophy.

2021

Path-specific effects in mediation analysis provide a useful tool for fairness analysis, which is mostly based on nested counterfactuals. However, the dictum “no causation without manipulation” implies that path-specific effects might be induced by certain interventions. This paper proposes a new path intervention inspired by information accounts of causality, and develops the corresponding intervention diagrams and π-formula. Compared with the interventionist approach of Robins et al.(2020) based on nested counterfactuals, our proposed path intervention method explicitly describes the manipulation in structural causal model with a simple information transferring interpretation, and does not require the non-existence of recanting witness to identify path-specific effects. Hence, it could serve useful communications and theoretical focus for mediation analysis.

A cause may influence its effect via multiple paths. Paradigmatically (Hesslow, 1974), taking birth control pills both decreases one's risk of thrombosis by preventing pregnancy and increases it by producing a blood chemical. Building on Pearl (2001), I explicate the notion of a path-specific effect. Roughly, a path-specific effect of C on E via path P is the degree to which a change in C would change E were they to be transmitted only via P. Facts about such effects may be gleaned from the structural equations commonly used to represent the causal relationships among variables. I contrast my analysis of the Hesslow case with those given by theorists of probabilistic causality, who mistakenly link it to issues of causal heterogeneity, token-causation and indeterminism. The reason probabilistic theories misdiagnose this case is that they pay inadequate attention to the structural relationships among variables.

Canadian journal of psychiatry. Revue canadienne de psychiatrie, 2005

Path analysis is an extension of multiple regression. It goes beyond regression in that it allows for the analysis of more complicated models. In particular, it can examine situations in which there are several final dependent variables and those in which there are "chains" of influence, in that variable A influences variable B, which in turn affects variable C. Despite its previous name of "causal modelling," path analysis cannot be used to establish causality or even to determine whether a specific model is correct; it can only determine whether the data are consistent with the model. However, it is extremely powerful for examining complex models and for comparing different models to determine which one best fits the data. As with many techniques, path analysis has its own unique nomenclature, assumptions, and conventions, which are discussed in this paper.

Causal modeling generally involves the construction and use of diagrammatic representations of the causal assumptions, expressed as directed acyclic graphs (DAGs). Do such graphs have cognitive benefits, for example by facilitating user inferences involving the underlying causal models? In two empirical studies, participants were given a set of causal assumptions, then attempted to identify all the causal pathways linking two variables in the model implied by these causal assumptions. Participants who were provided with a path diagram expressing the assumptions were more successful at identifying indirect pathways than those given the assumptions in the form of lists. Furthermore, the spatial orientation of the causal flow in the graphical model (left to right or right to left) had effects on the speed and accuracy with which participants made these inferences.

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.