semiparametric doubly-robust quantile treatment effect estimator and empirical data on environmental regulation's impact on corporate digital transformation.

The research data encompass simulated data, simulation code, and an empirical dataset. The simulated data generate potential outcomes, observed outcomes, propensity scores, and treatment assignments under three distinct designs. The first two designs differ exclusively in their propensity score specification—linear versus nonlinear—while the third features unbalanced distributions. The simulation code implements inverse probability weighting (IPW) and doubly robust (DR) estimators for quantile treatment effects (QTE), with IPW relying on a linear parametric model and DR employing multiple machine learning algorithms for propensity score estimation, thereby validating the advantages of the semiparametric quantile DR estimator. The empirical analysis examines the causal effect of environmental regulation on corporate digital transformation, exploiting China's new energy city pilot policy as a quasi-natural experiment. Digital transformation levels are constructed through textual analysis of listed firms' annual reports. Treatment assignment is defined by region-year indicators. Confounding variables—including firm age, CEO duality, return on equity, standard audit opinion, operating revenue, total assets, and turnover ratio—serve as explanatory variables for propensity score estimation and the outcome distribution. The empirical specification employs a QTE doubly robust estimator with propensity scores estimated via random forest.