Reduction of Conditional Factors in Causal Analysis

Faced with a great number of conditional factors in big data causal analysis, the reduction algorithm put forward in this paper can reasonably reduce the number of conditional factors. Compared with the previous reduction methods, we take into consideration the influence of conditional factors on resulted factors, as well as the relationship among conditional factors themselves. The basic idea of the algorithm proposed in this paper is to establish the matrix of mutual deterministic degrees in between conditional factors. If a conditional factor f has a greater deterministic degree with respect to another conditional factor h, we will delete the factor h unless factor h has a greater deterministic degree with respect to f, then delete factor f in this case. With this reduction, we can ensure that the conditional factors participating in causal analysis are as irrelevant as possible. This is a reasonable requirement for causal analysis

Partisan Conflict and Income Inequality in the United States: A Nonparametric Causality-in-Quantiles Approach

This paper examines the predictive power of a partisan conflict on income inequality. Our study contributes to the existing literature by using the newly introduced nonparametric causality-in-quantile testing approach to examine how political polarization in the United States affects several measures of income inequality and distribution overtime. The study uses annual time-series data between the periods 1917–2013. We find evidence in support of a dynamic causal relationship between partisan conflict and income inequality, except at the upper end of the quantiles. Our empirical findings suggest that a reduction in partisan conflict will lead to a reduction in our measures of income inequality, but this requires that inequality is not exceptionally high