This paper discusses the problem of estimation and inference on  time-varying treatments . We propose a method for inference on  treatment histories, by introducing adynamic covariate balancing method  . Our approach allows for (i) treatments to propagate arbitrarily over time ; (ii) non-stationarity and heterogeneity of treatment effects ; (iii) high-dimensional covariates , and (iv) unknown propensity score functions . We study the asymptotic properties of the estimator, and we showcase the parametric convergence rate of the proposed procedure. We illustrate in simulations and an empirical application  the advantage of the method over state-of-the-art competitors.
This paper discusses the problem of estimation and inference on the effects of time-varying treatment . We propose a method for inference on the effects treatment 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.
A
the effects of
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treatments
treatment
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the effects
D
by introducing a
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dynamic
introducing a dynamic
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combined with penalized regression
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propagate arbitrarily over time
be assigned based on arbitrary past information, with the propensity score being unknown
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non-stationarity and heterogeneity of treatment effects
outcomes and time-varying covariates to depend on treatment trajectories
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, and
;
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unknown propensity score functions
heterogeneity of treatment effects
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showcase
derive
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We illustrate in simulations
Simulations
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illustrate