Counterfactual causal analysis on structured data

Data generated in a real-world business environment can be highly connected with intricate relationships among entities. Studying relationships and understanding their dynamics can provide deeper understanding of business events. However, finding important causal relations among entities is a daunting task with heavy dependency on data scientists. Also due to fundamental problem of causal inference it is impossible to directly observe causal effects. Thus, a method is proposed to explain predictive causal relations in an arbitrary linked dataset using counterfactual type causality. The proposed method can generate counterfactual examples with high fidelity in minimal time. It can explain causal relations among any chosen response variable and an arbitrary set of independent causal variables to provide explanations in natural language. The evidence of the explanations is shown in the form of a summarized connected data graph

Counterfactual causal analysis on structured data

Data generated in a real-world business environment can be highly connected with intricate relationships among entities. Studying relationships and understanding their dynamics can provide deeper understanding of business events. However, finding important causal relations among entities is a daunting task with heavy dependency on data scientists. Also due to fundamental problem of causal inference it is impossible to directly observe causal effects. Thus, a method is proposed to explain predictive causal relations in an arbitrary linked dataset using counterfactual type causality. The proposed method can generate counterfactual examples with high fidelity in minimal time. It can explain causal relations among any chosen response variable and an arbitrary set of independent causal variables to provide explanations in natural language. The evidence of the explanations is shown in the form of a summarized connected data graph

Toward a Utilitarian Theory of Consumer Response to Advertising Music

It is surprisingly difficult to know whether a piece of ad music will have its intended effect on consumers. A model developed by the author and a colleague (Craton & Lantos, 2011; Lantos & Craton, 2012) consists of four broad variables (listening situation, musical stimulus, listener characteristics, and listener’s advertising processing strategy) that interact to determine attitude toward the advertising music (Aam), a multidimensional construct that captures the many cognitive and affective elements of a consumer’s experience of ad music. Emerging research on negative emotional response to music, brand avoidance, and “mixed emotions” is consistent with predictions that Aam’s valence can be negative or a mixture of positive and negative (ambivalent). This literature also has implications for how to measure Aam and clarify its structure—specifically, the relationship between overall musical response and Aam’s many subsidiary elements. The present chapter reviews this emerging work, discusses its implications for the model, and suggests how the model can be extended by adding a layer of diverse psychological processes (“mechanisms”) that mediate between its four broad causal variables and Aam. The theory is “utilitarian” in the sense that the proposed mechanisms evolved to perform practical, biologically important tasks not specifically related to music processing.

WHAT WILL THE MUNICIPAL ELECTION BE LIKE? Crisis, Covid-19 and the voter

The possible characteristics of the forthcoming municipal election will be presented in this article. Using the explanation by mechanisms and construction of scenarios, we provide clarity to the coming dispute. Three variables are considered for construction of scenarios: 1) Polarization / Nationalization; 2) Political and economic crisis; 3) Covid-19. The possible effects on the voter of these three causal variables are presented. This article reveals the importance of using the conjuncture analysis, the construction of scenarios and the prospective analysis to explain the voter's behavior.

Rational Thoughts in Neural Codes

Complex behaviors are often driven by an internal model, which integrates sensory information over time and facilitates long-term planning to reach subjective goals. We interpret behavioral data by assuming an agent behaves rationally — that is, they take actions that optimize their subjective reward according to their understanding of the task and its relevant causal variables. We apply a new method, Inverse Rational Control (IRC), to learn an agent’s internal model and reward function by maximizing the likelihood of its measured sensory observations and actions. This thereby extracts rational and interpretable thoughts of the agent from its behavior. We also provide a framework for interpreting encoding, recoding and decoding of neural data in light of this rational model for behavior. When applied to behavioral and neural data from simulated agents performing suboptimally on a naturalistic foraging task, this method successfully recovers their internal model and reward function, as well as the computational dynamics within the neural manifold that represents the task. This work lays a foundation for discovering how the brain represents and computes with dynamic beliefs.

Data source combination for tourism demand forecasting

Search engine data are of considerable interest to researchers for their utility in predicting human behaviour. Recently, search engine data have also been used to predict tourism demand (TD). Models developed based on such data generate more accurate forecasts of TD than pure time-series models. The aim of this article is to examine whether combining causal variables with search engine data can further improve the forecasting performance of search engine data models. Based on an artificial neural network framework, 168 observations during 2005–2018 for short-haul travel from Hong Kong to Macau are involved in the test, and the empirical results suggest that search engine data models with causal variables outperform models without causal variables and other benchmark models.