Instrumental variable identification is a strategy in causal statistics for estimating the counterfactual effect of treatment D on output Y controlling for covariates \mathbf{X We introduce a de-biased machine learning (DML) approach to estimating complier parameters with high-dimensional data. Complier parameters include local average treatment effect, average complier characteristics, and complier counterfactual outcome distributions. In our approach, the de-biasing is itself performed by machine learning, a variant called automatic de-biased machine learning (Auto-DML).  We prove our estimator is consistent, asymptotically normal, and semi-parametrically efficient. In experiments, our estimator outperforms state-of-the-art alternatives, and it does not require ad hoc trimming or censoring of a learned propensity score. We use it to estimate the effect of 401(k) participation on the distribution of net financial assets.
 We introduce a de-biased machine learning (DML) approach to estimating complier parameters with high-dimensional data. Complier parameters include local average treatment effect, average complier characteristics, and complier counterfactual outcome distributions. In our approach, the de-biasing is itself performed by machine learning, a variant called automatic de-biased machine learning (Auto-DML). By regularizing the balancing weights, it does not require ad hoc trimming or censoring. We prove our estimator is consistent, asymptotically normal, and semi-parametrically efficient. We use the new approach to estimate the effect of 401(k) participation on the distribution of net financial assets.
D
Instrumental variable identification is a strategy in causal statistics for estimating the counterfactual effect of treatment D on output Y controlling for covariates \mathbf{X
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By regularizing the balancing weights, it does not require ad hoc trimming or censoring.
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In experiments, our estimator outperforms state-of-the-art alternatives, and it does not require ad hoc trimming or censoring of a learned propensity score. We use it
We use the new approach