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

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 .