Tools for Validating Causal and Predictive Claims in Social Science Models

Abstract To better understand and describe the world around them, and ultimately produce theories that can facilitate better decision making or policy interventions, social scientists need to be able to create, analyze, and validate social, political, and economic models that include causal and predictive elements. Causality is, however, notoriously difficult to analyze. This is especially true in social science due to the complexity of the phenomena being studied. To address this complexity, we describe a suite of causal/predictive analysis techniques adapted from a variety of social, natural, and computational science applications, specifically chosen for their unique applicability to the problems of analyzing temporally offset causes and effects. In particular, we describe four methods for analyzing predictive/causal claims: (1) Granger causality is a well-established method from econometrics that can identify relationships between temporally offset causes and effects when the offsets are fixed; (2) forward-only dynamic time-warping (DTW) addresses uneven temporal offsets; (3) convergent cross mapping (CCM) can be used to analyze bi-directional causality produced by the feedback relationships present in many social systems; (4) finally, we describe a novel feature-based qualitative pattern-recognition approach to identify and explain qualitative causal/predictive relationships that don’t fit more traditional correlation-based analysis techniques. Social scientists can use this mix of analytic techniques to validate (or more-precisely, to invalidate) their causal/predictive hypotheses and produce more robust understandings of complex systems. We present prototype software implementations of these analytic techniques and demonstrate the efficacy of our proposed approaches.