Causality by Vote: Aggregating Evidence on Causal Relations in Economic Growth Processes*

Abstract

In this paper, we investigate the performance of five causality-detection methods and the aggregation of their results when considering multiple units in a panel data setting. We employ voting rules as an aggregation procedure to determine which causal paths are identified for the sample population. Using both simulated and real-world panel data, we show the performance of these methods in detecting the correct causal paths by comparing them to a benchmark that represents a standard growth model as the ground truth.