Factor models for longitudinal data, where policy adoption is unconfounded with respect to a low-dimensional set of latent factor loadings, have become increasingly popular for causal inference. Most existing approaches, however, rely on a causal finite-sample approach or computationally intensive methods, limiting their applicability and external validity. In this paper, we propose a novel method for panel causal inference within a super-population framework, based on inverse propensity score weighting, where the propensity score is a function of latent factor loadings. The approach relaxes the traditional restrictive assumptions of causal panel methods, while offering advantages in terms of causal interpretability, policy relevance, and computational efficiency. Under standard assumptions, we develop a three-step estimation procedure for the ATT and derive its large-sample properties using M-estimation theory. Simulation results show that the proposed estimator remains reliable across a variety of designs, including settings with misspecified factor structure and weakly dependent errors. We apply the method to assess the causal effect of the Paris Agreement - a policy aimed at fostering the transition to a low-carbon economy - on European stock returns. Our empirical results suggest a statistically significant and negative short-run effect on the stock returns of firms that demonstrate a strong commitment to sustainability.

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