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Causal Diagrams As Well As Causal Mechanisms

There is a long history of the job of directed causal diagrams to stand upward for hypotheses well-nigh causation. Can the mathematics in...


There is a long history of the job of directed causal diagrams to stand upward for hypotheses well-nigh causation. Can the mathematics in addition to graphical systems created for statistical causal modeling live on adapted to stand upward for in addition to evaluate hypotheses well-nigh causal mechanisms in addition to outcomes?

In the causal modeling literature the construction of a causal hypothesis is something similar this: variable T increases/ decreases the probability of the occurrence of resultant E. This is the causal relevance criterion described yesteryear Wesley Salmon inward Scientific Explanation in addition to the Causal Structure of the World. It is a fundamentally statistical agreement of causality.

Here is a classic causal path model yesteryear Blau in addition to Duncan indicating the relationships amid a publish of causal factors inward bringing well-nigh an resultant of involvement -- "respondent's outset job".


This construction aims at joining a qualitative hypothesis well-nigh the causal relations amid a gear upward of factors amongst a quantitative measurements of the correlations in addition to conditional probabilities that back upward these causal relations. The whole construction oftentimes takes its source inward a multivariate regression model.

Aage Sørensen describes the underlying methodological premise of quantitative causal inquiry inward these terms in his contribution to Frontiers of Sociology (Annals of the International Institute of Sociology Vol. 11):
Understanding the association betwixt observed variables is what most of us believe inquiry is about. However, nosotros rarely worry well-nigh the functional shape of the relationship. The primary ground is that nosotros rarely worry well-nigh how nosotros acquire from our ideas well-nigh how modify is brought about, or the mechanisms of social processes, to empirical observation. In other words, sociologists rarely model mechanisms explicitly. In the few cases where they produce model mechanisms, they are labeled mathematical sociologists, non a real large or of import specialty inward sociology. (370)
My query hither is whether this scheme of representation of causal relationships in addition to the graphical schemes that ask maintain developed around it are useful for the analytics of causal mechanisms.

The background metaphysics assumed inward the causal modeling literature is Humean in addition to "causal-factor" based; such-and-so element increases the probability of occurrence of an resultant or an intermediate variable, the simultaneous occurrence of H5N1 in addition to B increment the probability of the outcome, etc. Quoting Peter Hedstrom on causal modeling:
In the words of Lazarsfeld (1955: 124-5), "If nosotros ask maintain a human relationship betwixt x in addition to y; in addition to if for whatsoever antecedent seek out element the partial relationships betwixt x in addition to y produce non disappear, in addition to so the master human relationship should live on called a causal one." (Dissecting the Social: On the Principles of Analytical Sociology)
The electrical current iteration of causal modeling is a directed acyclic graph (DAG). Felix Elwert provides an accessible introduction to directed acyclic graphs inward his contribution to Handbook of Causal Analysis for Social Research (link). Here is a curt description provided yesteryear Elwert:
DAGs are visual representations of qualitative causal assumptions: They encode researchers’ adept cognition in addition to beliefs well-nigh how the footing works. Simple rules in addition to so map these causal assumptions onto statements well-nigh probability distributions: They break the construction of associations in addition to independencies that could live on observed if the information were generated according to the causal assumptions encoded inward the DAG. This translation betwixt causal assumptions in addition to observable associations underlies the 2 primary uses for DAGs. First, DAGs tin dismiss live on used to evidence or disprove the identification of causal effects, that is, the possibility of computing causal effects from observable data. Since identification is e'er conditional on the validity of the assumed causal model, it is fortunate that the minute primary job of DAGs is to acquaint those assumptions explicitly in addition to break their testable implications, if any. (246)
H5N1 DAG tin dismiss live on interpreted equally a non-parametric structural equation model, according to Elwert. (Non-parametric hither agency precisely that nosotros produce non assume that the information are distributed normally.) Elwert credits the evolution of the logic of DAGs to Judea Pearl in addition to Peter Spirtes, along amongst other researchers inside the causal modeling community.

Johannes Textor in addition to a team of researchers ask maintain implemented DAGitty, a platform for creating in addition to using DAGs inward appropriate fields, including particularly epidemiology (link). H5N1 crucial characteristic of DAGitty is that it is non entirely a graphical computer program for drawing graphs of possible causal relationships; rather, it embodies an underlying logic which generates expected statistical relationships amid variables given the stipulated relationships on the graph. Here is a screenshot from the platform:



The query to consider hither is whether in that place is a human relationship betwixt the methodology of causal mechanisms in addition to the causal theory reflected inward these causal diagrams. 

It is apparent that the underlying ontological assumptions associated amongst the 2 approaches are quite different. Causal mechanisms theory is mostly associated amongst a realist approach to the social world, in addition to mostly rejects the Humean theory of causation. The causal diagram approach, yesteryear contrast, is premised on the Humean in addition to statistical approach to causation.  A causal mechanisms hypothesis is non fundamentally evaluated inward damage of the statistical relationships amid a gear upward of variables; whereas a measure causal model is wholly intertwined amongst the mathematics of conditional correlation.

Consider a few examples. Here is a complex graphical representation of a procedure understood inward damage of causal mechanisms from McGinnes in addition to Elandy, "Unintended Behavioural Consequences of Publishing Performance Data: Is More Always Better?" (link):



Plainly this model is impossible to evaluate statistically yesteryear attempting to mensurate each of the variables; instead, the researchers continue yesteryear validating the private mechanisms identified hither equally good equally the administration of influence they ask maintain on other intermediate outcomes. The resultant of involvement is "quality of learning" at the pump of the graph; in addition to the diagram attempts to stand upward for the complex construction of causal influences that be amid several dozen mechanisms or causal factors.

Here is some other representative of a causal mechanisms path diagram, this fourth dimension representing the causal organisation involved inward drought in addition to mental wellness yesteryear Vins, Bell, Saha, in addition to Hess (link).


Here also the model is non offered equally a statistical representation of covariance amid variables; rather, it is a hypothetical sketch of the factors which play inward mechanisms leading from drought to depression in addition to anxiety inward a population. And the assessment of the model should non accept the shape of a statistical evaluation (a non-parametric structural equation model), but rather a piecemeal verification of the validity of the specific mechanisms cited. (John Gerring argues that this is a major weakness inward causal mechanisms theory, however, inward "Causal Mechanisms? Yes, But ..." (link).)

It seems, therefore, that the superficial similarity betwixt a causal model graph (a DAG) in addition to a causal mechanisms diagram is only skin-deep. Fundamentally the 2 approaches brand real different assumptions well-nigh both ontology (what a causal human relationship is) in addition to epistemology (how nosotros should empirically evaluate a causal claim). So it seems unlikely that it volition live on fruitful for causal-mechanisms theorists to seek to accommodate methods similar DAGs to stand upward for the causal claims they desire to advance in addition to evaluate.

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