74 – Statistical Methods and Applications: Domestic and International
Impacting Policy by Estimating Causal Links
Hrishikesh D. Vinod
Fordham University
Statistics makes an important impact on society by analyzing quantitative evidence related to public policy issues regarding socioeconomic well-being which must be based only on non-experimental data. Suppes' probabilistic causality theory establishes inequalities among probabilities of events. Instead of events, our ``cause' is a self-driven data generating process (DGP). We develop three criteria based on properties residuals of flipped kernel regression conditional expectation functions, Ef(X_j|X_i, X_k) and Ef(X_i|X_j, X_k). Our unanimity index aggregates measures of four orders of stochastic dominance and new asymmetric partial correlation coefficients, which yields decision rules for quantifying percent support for the competing causal paths X_i-->X_j, X_j--> X_i, X_i <--> X_j. A simulation supports our decision rules illustrated by many real-world examples, including the causes of US recession, policies for encouraging private investment in India and assessment of effective advertising media.