Indirect Causal Effects Research Articles

Women, Labour, and Public Policy: Female Labour Market Integration in OECD Countries. A Comparative Perspective

AbstractThis article investigates the influence of political measures and instruments on women's employment in OECD countries. The policy dimension builds the central explanatory category filling the ‘black box’ between political institutions and the decision-making process on the one hand, and policy outcome on the other. Unlike former comparative studies on the relationship between women's employment and public policies, this article analyses a much larger country sample, looking at 28 OECD countries as well as a broader range of policies. Additionally, path analyses are conducted, modelling direct and indirect causal effects on women's labour market integration. The analyses show that while the cultural, economic and political framework can create a positive environment for women's employment in general, ‘women-friendly’ public policies are important, and are necessary for the more intensive and egalitarian labour market integration of women compared to that of men.

Toward Causal Inference With Interference

A fundamental assumption usually made in causal inference is that of no interference between individuals (or units); that is, the potential outcomes of one individual are assumed to be unaffected by the treatment assignment of other individuals. However, in many settings, this assumption obviously does not hold. For example, in the dependent happenings of infectious diseases, whether one person becomes infected depends on who else in the population is vaccinated. In this article, we consider a population of groups of individuals where interference is possible between individuals within the same group. We propose estimands for direct, indirect, total, and overall causal effects of treatment strategies in this setting. Relations among the estimands are established; for example, the total causal effect is shown to equal the sum of direct and indirect causal effects. Using an experimental design with a two-stage randomization procedure (first at the group level, then at the individual level within groups), unbiased estimators of the proposed estimands are presented. Variances of the estimators are also developed. The methodology is illustrated in two different settings where interference is likely: assessing causal effects of housing vouchers and of vaccines.

Ramifications: An Extension and Correspondence Result for the Event Calculus

Classical logic Event Calculus, and the special purpose logical action language Formula, are both well established formalisms for describing actions and change. However, there is yet to be an account of ramifications in Event Calculus sufficiently general to represent the classes of domains expressible in Formula. Indeed, an adequately general ramification theory constructed in any general purpose logical language still awaits. Therefore, under the motivation of creating a flexible ramification theory in a universal language, suitable for integration into a rich action theory, a new enhanced version of classical logic Event Calculus named EC-R is proposed. EC-R supports representation and reasoning about domains containing ramifications for classes of domains more general than those possible under previous general purpose language formulations. This article makes two main contributions. The first, EC-R, is a narrative-based action formalism able to represent concurrent events, non-deterministic actions and indirect causal effects by virtue of an integrated solution to the frame and ramification problems. The formalism can reason about significant subclasses of domains containing both mutually interacting effects and cyclic causal dependencies. The formalism is elaboration tolerant and may be integrated with the standard variants of the Event Calculus. The second contribution is the definition of a semantic mapping between EC-R and Formula, and a proof of soundness and completeness of the EC-R theory with respect to Formula's model theoretic specification.

Topological keystone species complexes in ecological interaction networks

Advanced techniques of network analysis allow the quantification of the indirect interactions and the topological importance of components in ecological interaction networks. In current conservation biology, considering indirect causal effects, identifying keystone species and outlining multispecies approaches begin to be high priority goals. We make an attempt to connect these issues within a network context. Our main interest is to determine the positionally most important set of n nodes in a network, to analyse whether a set of nodes for small n is a subset of another for larger n, and to quantify this nestedness. We apply the KeyPlayer software, a novel tool for network analysis in ecology, introduced originally in mathematical sociology. Topological keystone species complexes are defined, we illustrate the use of this method in a case study and analyse a database of 9 plant-pollinator interaction networks. Our main conclusion is that multispecies approaches may give results very different from single species analyses.

End-User Acceptance of a Learning Management System in Two Hybrid Large-Sized Introductory Undergraduate Courses: A Case Study

Drawing from the Technology Acceptance Model, this correlational study was intended to determine student users' adoption of WebCT and its relationship to their final grade. In acknowledging the relevance of human-computer interaction, the author attempted to investigate a belief-attitude-behavior relationship in a WebCT setting. Given a social learning approach, two external variables affecting the relationship were taken into account. The qualitative inquiry involved two large-sized undergraduate hybrid courses with a total of 469 students participating. To model both direct and indirect causal effects as well as measurement error, path analysis was conducted, using structural equation modeling. Implications for practitioners and researchers and addressed.

Direct and Indirect Causal Effects via Potential Outcomes*

Abstract. The use of the concept of ‘direct’ versus ‘indirect’ causal effects is common, not only in statistics but also in many areas of social and economic sciences. The related terms of ‘biomarkers’ and ‘surrogates’ are common in pharmacological and biomedical sciences. Sometimes this concept is represented by graphical displays of various kinds. The view here is that there is a great deal of imprecise discussion surrounding this topic and, moreover, that the most straightforward way to clarify the situation is by using potential outcomes to define causal effects. In particular, I suggest that the use of principal stratification is key to understanding the meaning of direct and indirect causal effects. A current study of anthrax vaccine will be used to illustrate ideas.

Testing a model of science process skills acquisition: An interaction with parents' education, preferred language, gender, science attitude, cognitive development, academic ability, and biology knowledge

AbstractPath analysis techniques were used to test a hypothesized structural model of direct and indirect causal effects of student variables on science process skills. The model was tested twice using data collected at the beginning and end of the school year from 67 9th‐ and 10th‐grade biology students who lived in a rural Franco‐American community in New England. Each student variable was found to have significant effects, accounting for approximately 80% of the variance in science process skills achievement. Academic ability, biology knowledge, and language preference had significant direct effects. There were significant mediated effects by cognitive development, parents' education, and attitude toward science in school. The variables of cognitive development and academic ability had the greatest total effects on science process skills. Implications for practitioners and researchers are discussed.

THE GENESIS OF SIZE HIERARCHIES IN SEEDLING POPULATIONS OF IMPATIENS CAPENSIS MEERB.

SummaryIn 1978 and 1980 I planted 660 and 900 seeds of Impatiens capensis Meerb. at four densities in the greenhouse to examine how variation in plant performance is generated. I noted seed weight, seed type (cleistogamous or chasmogamous), maternal parent, and population of origin, and monitored emergence date, cotyledon area, the biomass of competitors within the same flat, and the final size reached by the seedlings after 63 to 80 d of growth. Larger seeds tended to germinate sooner, as did seeds derived from chasmogamous flowers. Seeds from northern populations took longer to germinate. Seeds from different maternal parents also germinated at different rates. Cotyledon area strongly depended on seed weight, and, to a lesser extent, germination date. Maternal parent and seed type significantly affected cotyledon area in 1980.Plant density and cotyledon area influenced final size the most, but almost every factor proved to be statistically significant. As expected, earlier emerging seedlings with larger cotyledons growing at the lowest density grew into the largest plants. I applied path analysis to summarize the interactions among the continuous variables and to disentangle direct from indirect causal effects. This technique revealed that seed weight enhanced performance primarily through its effects on emergence date and cotyledon area. Chasmogamous seedlings outperformed cleistogamous seedlings, and their advantage was expressed during most phases of growth. Together, the predictor variables accounted for over four‐fifths of the total variation in final size.The inequality of plant sizes, as measured by the Gini coefficient, increased appreciably during the course of the experiment. There was no simple relation to density, however, suggesting that both intrinsic differences in growth rate, and competitive dominance and supression fuel the establishment of size hierarchies.

SEINE: Standard Errors of Indirect Effects

In his early exposition of path analysis, Duncan (1966, p. 7) noted that the method "provides a calculus for indirect effects." Despite the interest in indirect causal effects, most users treat them as if they are population parameters and do not test whether they are statistically significant. Sobel (1982) has recently derived the asymptotic distribution of indirect effects. This paper describes a new FORTRAN IV computer program that provides the causal modeler with standard errors of indirect effects for recursive causal models. The only input data required are in the form of a matrix of structural parameter estimates along with their variances and covariances.

政治参加行動における1つの因果的連関モデルの検討

The hypothesis examined in the present study is whether SES variables have not only direct causal effect upon political participation but also indirect causal effects mediated by political attitudes in a wide sense. This hypothesis was tested in a nation-wide survey, conducted in December, 1975 (N=2164). The results are as follows: (a) Among SES variables, education has the strongest direct and indirect causal effects upon political participation. (b) Occupation has a rather strong direct causal effect upon political participation, but has weak indirect effects. (c) Income has a weak direct causal effect upon political participation, but has strong indirect causal effects resulting to influence political attitudes, and thus leading to influence political participation.

Indirect Effects in Path Analysis

The indirect effect in path analysis has been defined as equal to the difference between the correlation coefficient and the path coefficient. This definition has led to ambiguous and inconsistent interpretations of indirect effects across various applications. This paper shows that what was earlier defined as the indirect effect may, in fact, be composed of three parts: (1) the indirect effect, (2) spurious association due to joint dependence on a prior variable, and (3) association due to the correlation between predetermined variables. We argue for a redefinition of the indirect effect so that only those paths which can be traced through intervening variables are included in the indirect effect term. Indirect effects can then be interpreted consistently across all applications as being indirect causal effects.