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Path Model
Published in Jhareswar Maiti, Multivariate Statistical Modeling in Engineering and Management, 2023
The steps that can be followed in developing a path model and conducting path analysis are summarized below. Define the system and its associated problems, and identify the variables of interest.Develop the theoretically based conceptual path model (e.g., recursive or non-recursive) and respective path diagram (e.g., Figure 10.2b or Figure 10.3).Construct path equations representing the causal structure (also called structural equations).Collect data that is representative of the system’s behavior of interest (e.g., the sample size should be adequate).Estimate the parameters.Evaluate adequacy (goodness of fit) of the model.Interpret the results and make decisions.
Search for Causal Models
Published in Marloes Maathuis, Mathias Drton, Steffen Lauritzen, Martin Wainwright, Handbook of Graphical Models, 2018
Understanding causal relations can enable making predictions under interventions, be used as input to algorithms for constructing interventions to achieve certain objectives, and provide an understanding of how changes have been brought about. A traditional way to discover causal relations is to use interventions or randomized experiments, which is, however, in many cases of interest too expensive, too time-consuming, unethical, or even impossible. Therefore, inferring the underlying causal structure from purely observational data, or from combinations of observational and experimental data, has drawn much attention in computer science, statistics, philosophy, neuroscience, and other disciplines. With the rapid accumulation of huge volumes of data, it is even more desirable to abstract causal knowledge from data, and it is necessary to develop automatic causal search algorithms that scale well.
The Golem of Prague
Published in Richard McElreath, Statistical Rethinking, 2020
An important feature of these process models is that they express causal structure. Different process models formalize different cause and effect relationships. Whether analyzed mathematically or through simulation, the direction of time in a model means that some things cause other things, but not the reverse. You can use such models to perform experiments and probe their causal implications. Sometimes these probes reveal, before we even turn to statistical inference, that the model cannot explain a phenomenon of interest.
Prioritizing collaborative scheduling practices based on their impact on project performance
Published in Construction Management and Economics, 2022
Chuanni He, Min Liu, Thais da C. L. Alves, Natalie M. Scala, Simon M. Hsiang
Using the abovementioned concepts, Chow and Liu (1968) developed a tree-shaped graphic model to illustrate the causal relationship between variables. The model consists of nodes corresponding to different variables, and the links connecting two nodes represents the causal structure. According to the literature, a Chow–Liu tree model can be developed using the following steps (Schaffernicht et al. 2007):Calculate the pairwise MI between each variable Assign each link a weight of to develop a maximum-weight spanning tree.Start from the pairs with maximum MI connection and add further nodes with the next highest I value. Ignore any edges that lead to a cycle.Choose a variable as the root node.
The Impact of Cross-Addiction on Information Sharing Behaviors on Social Networking Sites
Published in Journal of Computer Information Systems, 2019
Babajide Osatuyi, Star Roxanne Hiltz
To test hypothesis 3, a structural equation model was estimated to both visualize and establish the causal structure of the proposed relationships (as shown in Figure 3). The standardized path coefficients in the structural model including the moderating effects of alcohol use were all significant at the 0.01 level, therefore providing support for Hypothesis 3. The path coefficients to self-promotion (0.30, p = 0.000) and peer promotion (0.34, p = 0.001) from SNS addiction are lower when the moderator (i.e., alcohol use) is not included in the model compared to when it is included, where both links were (0.37, p = 0.001) and (0.36, p = 0.000), respectively. The variabilities explained with the moderator are 39% for self-promotion and 41% for peer promotion versus 9% and 12%, respectively, for the model without the moderator. These results further provide support for Hypothesis 3.
Implementation of path analysis and piecewise structural equation modelling to improve the interpretation of key performance indicators in team sports: An example in professional rugby union
Published in Journal of Sports Sciences, 2021
Andrew R. Novak, Franco M. Impellizzeri, Cathal Garvey, Job Fransen
Any causal interpretation of associations requires the definition of causal structure. Clearly, even in this case but in absence of experimental corroboration, the estimates are just potentially less biased, and the interpretation should account for this limitation. But using arrows to hypothesise the direction of association, we show that points margin is the only reasonable direct cause of match outcome, while points margin is caused only by the difference between points scored and points conceded. Further, the only variables we could logically hypothesise to have direct causal associations with point-scoring/conceding are tries, conversions, penalty goals and drop goals. All other KPIs could realistically only cause match outcomes through indirect pathways which pass through one or more point-scoring variables, i.e., it would not be logical to hypothesise (or model) a direct pathway between linebreaks, completed passes, or average metres per carry, vs successful match outcomes. Each of these may contribute to the scoring of a try or obtaining strong field positioning which ultimately ends in a penalty goal if a penalty kick is obtained. Therefore, it would be inappropriate to model the data in a manner that implies direct pathways between all KPIs and match outcomes. In addition to the direct pathways in our model (Figure 1a), we can also hypothesise relationships of unspecified direction between tries scored and penalties scored. This is because if a team obtains a penalty in the attacking zone, they may choose to take a penalty kick rather than attempting to score a try, thereby likely reducing the total tries scored by that team. Similarly, the scoring of a try may mean that a decision was made to kick the penalty into touch to obtain a lineout and therefore less penalty goals are scored. Additionally, the effect of tries on points scored is also partially mediated by scoring conversions, i.e., if a try is scored, there is a direct effect on points, while a secondary pathway is also present to potentially score another two points if the conversion kick is successful. This second pathway would be expected to have some level of residual error due to the lack of including the location of the kick, quality of the kicker, and environmental conditions (e.g., wind) within the simplistic model; however, these sources of error can be hypothesised and tested in the appropriate place within the model.