Abstract
<title>Abstract</title> <p>Anti-democratic attitudes (ADA) threaten the stability of democratic systems. Several behavioral interventions have been proposed to strengthen democratic attitudes, yet the heterogeneity in their effectiveness remains unclear. In particular, it is unknown for whom these interventions are most effective, raising concerns that uniform approaches may benefit some groups while leaving others behind. Here, we use causal machine learning to analyze individual-level differences in the effectiveness of 25 behavioral interventions aimed to reduce ADA using data from a large-scale randomized controlled trial (N = 32,059). First, we uncover substantial heterogeneity of the interventions. For example, even the least-effective intervention still reduces ADA for 28% of individuals. Second, we identify key drivers across demographic and ideological characteristics, finding that political ideology is a strong, consistent predictor, even across party lines. For example, interventions based on factual correction are more effective for conservatives, while interventions appealing to institutional trust are more effective for liberals. Our findings highlight the limitations of one-size-fits-all strategies in reducing ADA and emphasize the importance of accounting for heterogeneity to maximize the effectiveness of democracy-strengthening interventions. Overall, these findings raise important equity concerns, as uniform intervention strategies may disproportionately benefit some groups but may systematically fail to reach vulnerable subgroups.</p>