We consider the estimation of treatment effects in settings when multiple treatments are assigned over time and treatments can have a causal effect on future outcomes. We formulate the problem as a linear state space Markov process with a high dimensional state and propose an extension of the double/debiased machine learning framework to estimate the dynamic effects of treatments. Our method allows the use of arbitrary machine learning methods to control for the high dimensional state, subject to a mean square error guarantee, while still allowing parametric estimation and construction of confidence intervals for the dynamic treatment effect parameters of interest. Our method is based on a sequential regression peeling process, which we show can be equivalently interpreted as a Neyman orthogonal moment estimator. This allows us to show root-n asymptotic normality of the estimated causal effects.

NEW YORK (TheStreet) --As tech titans and media moguls gather in Idaho's Sun Valley, CNBC's Julia Boorstin sat down with LinkedIn (LNKD) co-founder Reid Hoffman to discuss the future of artificial intelligence today on "Closing Bell." "I think artificial intelligence is going to be transforming massive swaths of the area of work in the economy. Ten to twenty years from now if you're going to be an effective lawyer, doctor, or financial analyst, it will be in part your ability to use the technological implements, loosely going under the name of artificial intelligence," Hoffman explained. "I believe it could have very positive effects, but it does have very dynamic effects. Jobs will be going away and we have got to focus on entrepreneurship and creating new jobs. However, the jobs that do exist will be technology enabled and part of the skill set is having the necessary technology skills," Hoffman said.