Some comments about cognitions as causal variables.

1990 ◽  
Vol 45 (8) ◽  
pp. 984-985 ◽  
Author(s):  
G. R. Patterson
Keyword(s):  
Causal Variables

Case Formulation and Design of Behavioral Treatment Programs

2003 ◽  
Vol 19 (3) ◽  
pp. 164-174 ◽  
Author(s):  
Stephen N. Haynes ◽  
Andrew E. Williams
Keyword(s):  
Functional Analysis ◽  
Behavior Problems ◽  
Behavioral Treatment ◽  
Clinical Case ◽  
Relative Magnitude ◽  
Mechanisms Of Action ◽  
Treatment Programs ◽  
Case Formulation ◽  
Functional Relations ◽  
Causal Variables

Summary: We review the rationale for behavioral clinical case formulations and emphasize the role of the functional analysis in the design of individualized treatments. Standardized treatments may not be optimally effective for clients who have multiple behavior problems. These problems can affect each other in complex ways and each behavior problem can be influenced by multiple, interacting causal variables. The mechanisms of action of standardized treatments may not always address the most important causal variables for a client's behavior problems. The functional analysis integrates judgments about the client's behavior problems, important causal variables, and functional relations among variables. The functional analysis aids treatment decisions by helping the clinician estimate the relative magnitude of effect of each causal variable on the client's behavior problems, so that the most effective treatments can be selected. The parameters of, and issues associated with, a functional analysis and Functional Analytic Clinical Case Models (FACCM) are illustrated with a clinical case. The task of selecting the best treatment for a client is complicated because treatments differ in their level of specificity and have unequally weighted mechanisms of action. Further, a treatment's mechanism of action is often unknown.

scholarly journals Surrogate minimal depth as an importance measure for variables in random forests

2019 ◽  
Vol 35 (19) ◽  
pp. 3663-3671 ◽  
Author(s):  
Stephan Seifert ◽  
Sven Gundlach ◽  
Silke Szymczak
Keyword(s):  
Variable Selection ◽  
Supplementary Information ◽  
Predictor Variables ◽  
Importance Measure ◽  
Surrogate Variables ◽  
Machine Learning Approach ◽  
Complex Relationships ◽  
Minimal Depth ◽  
Insight Into ◽  
Causal Variables

Abstract Motivation It has been shown that the machine learning approach random forest can be successfully applied to omics data, such as gene expression data, for classification or regression and to select variables that are important for prediction. However, the complex relationships between predictor variables, in particular between causal predictor variables, make the interpretation of currently applied variable selection techniques difficult. Results Here we propose a new variable selection approach called surrogate minimal depth (SMD) that incorporates surrogate variables into the concept of minimal depth (MD) variable importance. Applying SMD, we show that simulated correlation patterns can be reconstructed and that the increased consideration of variable relationships improves variable selection. When compared with existing state-of-the-art methods and MD, SMD has higher empirical power to identify causal variables while the resulting variable lists are equally stable. In conclusion, SMD is a promising approach to get more insight into the complex interplay of predictor variables and outcome in a high-dimensional data setting. Availability and implementation https://github.com/StephanSeifert/SurrogateMinimalDepth. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Evaluating Hemato_4 (HCT), Hemato_5 (PLAT 1000), Hemato_9 (MCHC), and Biochem_20 (Alkaline Phosphatase) as biomarkers and possible mediators of the relationship between Xerosis and Sulfur Mustard

2018 ◽  
Vol 5 (6) ◽  
Author(s):  
Marjan Zare ◽  
Zaher Khazaei ◽  
Soghrat Faghihzadeh ◽  
Shohreh Jalaie ◽  
Malihe Sohrabivafa
Keyword(s):  
Indirect Effects ◽  
Sulfur Mustard ◽  
Bootstrap Method ◽  
Control Group ◽  
Case Group ◽  
Historical Cohort ◽  
Causal Pathways ◽  
The Relationship ◽  
Causal Variables

Background: Estimating total, direct, and indirect effects of causal variables on outcome variables can be done through proposed mediator guides on causal pathways. This can improve the efficiency of diagnostic efforts done by clinicians. In this study, the relationship between Sulfur Mustard and Xerosis was investigated, specifically with respect to the role of Hemato_4, Hemato_5, Hemato_9, and Biochem_20 biomarkers as mediators. Methods and Results: This was a historical cohort study (in Sardasht, Iran) which consisted of 492 subjects; 129 subjects were not exposed to Mustard Gas (control group) and 363 subjects were exposed to it (case group). Mediation models along with bootstrap method were used to evaluate the mediator validity of Hemato_4, Hemato_5, Hemato_9, and Biochem_20 on the relationship between Sulfur Mustard and Xerosis. The direct effects of Sulfur Mustard, via Hemato_4 and Hemato_9, on Xerosis were also significant (P<0.05 for each). However, there was no significant effects mediated by Hemato_5 or Biochem_20 (P>0.05 for each). While there were non-significant indirect effects of Sulfur Mustard on Xerosis by these latter biomarkers (P>0.05 for each), the first two biomarkers were, indeed, partial mediators. Conclusion: Sulfur Mustard can affect Xerosis through several single immune biomarker. However, as an intervention, sulfur mustard should affect several biomarkers through various mechanisms. Therefore, effects through multiple mediators, instead of single ones, may be more rational in the treatment strategy for xerosis.  

Regimes and the limits of realism: regimes as autonomous variables

1982 ◽  
Vol 36 (2) ◽  
pp. 497-510 ◽  
Author(s):  
Stephen D. Krasner
Keyword(s):  
Regime Change ◽  
Billiard Ball ◽  
Tectonic Plates ◽  
Distribution Of Power ◽  
The Relationship ◽  
Intervening Variables ◽  
Causal Variables

Two distinct traditions have developed from structural realist perspectives. The first, the billiard ball version, focuses purely on interaction among states. The second, the tectonic plates version, focuses on the relationship between the distribution of power and various international environments. It is the latter tradition that suggests why regimes may be important for a realist orientation. However, it also opens the possibility for viewing regimes as autonomous, not just as intervening, variables. There may be lags between changes in basic causal variables and regime change. There may be feedback from regimes to basic causal variables. Both lags and feedback suggest an importance for regimes that would be rejected by conventional structural arguments.

Immigrant Earnings: Cuban and Mexican Immigrants in the United States

1980 ◽  
Vol 14 (3) ◽  
pp. 315-341 ◽  
Author(s):  
Alejandro Portes ◽  
Robert L. Bach
Keyword(s):  
United States ◽  
Mexican Immigrants ◽  
The United States ◽  
Recent Immigrants ◽  
Theoretical Perspectives ◽  
Specific Goal ◽  
The Moment ◽  
Causal Variables

This study examines the determinants of earnings among two groups of recent immigrants—Cubans and Mexicans—interviewed at the moment of arrival in the United States and reinterviewed three years later. The specific goal is to examine the applicability to these results of causal variables suggested by recent alternative theoretical perspectives on income attainment.

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