Instrumental variable identification is a concept in causal statistics for estimating the counterfactual effect of treatment D on output Y controlling for covariates X using observational data. Even when measurements of (Y,D) are confounded , the treatment effect on the subpopulation of compliers can nonetheless be identified if an instrumental variable Z is available , which is independent of (Y,D) conditional on X and the unmeasured confounder . 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  de-biased machine learning via regularized Riesz representers (DML-RRR ). We prove our estimator is consistent, asymptotically normal, and semi-parametrically efficient. In experiments, our estimator outperforms state of the art alternatives . We use it to estimate the effect of 401(k) participation on the distribution of net financial assets.
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 using observational data. Even in the presence of an unmeasured confounder of (Y,D) , the treatment effect on the subpopulation of compliers can nonetheless be identified if an instrumental variable Z is available . 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.
R
concept
strategy
R
X
\mathbf{X
R
when measurements of
in the presence of an unmeasured confounder of
D
are confounded
D
, which is independent of (Y,D) conditional on X and the unmeasured confounder
A
automatic
R
via regularized Riesz representers (DML-RRR
(Auto-DML
R
state of the art alternatives
state-of-the-art alternatives, and it does not require ad hoc trimming or censoring of a learned propensity score