This paper presents a practical method of estimation and inference for the policy-relevant treatment effects. The policy-relevant treatment effects may not be estimated at the regular parametric \sqrt{n and Vytlacil, 2010). In this light, we propose a regularized estimator. The regularization improves the convergence rate when the rate is slower than \sqrt{n . Our proposed method is fully data-driven, and yields point estimates with smaller standard errors. Simulation studies based on the data generating process of Heckman and Vytlacil (2005, pp. 683) demonstrate that our regularized estimator achieves 10 to 300 times as small root mean squared errors as the estimator without regularization. At the same time, our confidence intervals achieve the desired coverage probability in the simulations .
This paper presents a practical method of estimation and inference for the policy-relevant treatment effects. Based on the characterization of this treatment parameter as a weighted average derivative (Heckman and Vytlacil, 2001, 2005, 2007), we apply the methods and theory of average derivative estimation to the framework of the policy-relevant treatment effects . Our proposed method yields point estimates with valid standard errors. Simulation studies confirm our theoretical results .
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The policy-relevant treatment effects may not be estimated at the regular parametric \sqrt{n
Based on the characterization of this treatment parameter as a weighted average derivative (Heckman
R
2010). In this light, we propose a regularized estimator. The regularization improves the convergence rate when the rate is slower than \sqrt{n
2001, 2005, 2007), we apply the methods and theory of average derivative estimation to the framework of the policy-relevant treatment effects
D
is fully data-driven, and
R
smaller
valid
R
based on the data generating process of Heckman and Vytlacil (2005, pp. 683) demonstrate that our regularized estimator achieves 10 to 300 times as small root mean squared errors as the estimator without regularization. At the same time, our confidence intervals achieve the desired coverage probability in the simulations
confirm our theoretical results