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Title: Dynamic covariate balancing: Estimating treatment effects over time Authors:  Davide Viviano - Stanford University (United States) [presenting]
Abstract: The problem of estimation and inference on the effects of time-varying treatment is discussed. We propose a method for inference on the treatment effects histories, introducing a dynamic covariate balancing method combined with penalized regression. Our approach allows for (i) treatments to be assigned based on arbitrary past information, with the propensity score being unknown; (ii) outcomes and time-varying covariates to depend on treatment trajectories; (iii) high-dimensional covariates; (iv) heterogeneity of treatment effects. We study the asymptotic properties of the estimator, and we derive the parametric convergence rate of the proposed procedure. Simulations and an empirical application illustrate the advantage of the method over state-of-the-art competitors.