Treatment Effect on the Association between Outcomes

This paper introduces Treatment Effect on the Association between Outcomes (TEA), a new causal estimand that measures how a treatment influences the covariance between two post-treatment variables. TEA enables researchers to estimate how interventions affect associations that characterize social inequalities. I define TEA, provide identification results under standard causal inference assumptions, and outline estimation strategies including regression-imputation, weighting, and double machine learning estimators. I compare and contrast TEA with other common estimands in similar research settings, highlighting its unique use. I demonstrate the use of TEA through two applications: the effect of college completion on income gradient in health and the effect of college completion on issue alignment, using NLSY97 and GSS respectively. By exploring how treatments modify associations between outcomes, TEA offers a valuable tool for sociological research on inequality, stratification, and public opinion, providing insights into the mechanisms sustaining social inequalities and informing policy interventions.