arXiv 29 May 2018 · Econometrics · publishedJournal of Econometrics (2021) · 2 citations (OpenAlex)
arXiv:1805.11503 · PDF · DOI · OpenAlex · Extracted main text
The policy relevant treatment effect (PRTE) measures the average effect of switching from a status-quo policy to a counterfactual policy. Estimation of the PRTE involves estimation of multiple preliminary parameters, including propensity scores, conditional expectation functions of the outcome and covariates given the propensity score, and marginal treatment effects. These preliminary estimators can affect the asymptotic distribution of the PRTE estimator in complicated and intractable manners. In this light, we propose an orthogonal score for double debiased estimation of the PRTE, whereby the asymptotic distribution of the PRTE estimator is obtained without any influence of preliminary parameter estimators as far as they satisfy mild requirements of convergence rates. To our knowledge, this paper is the first to develop limit distribution theories for inference about the PRTE.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Carneiro, P. and S. Lee (2009) Estimating Distributions of Potential Outcomes Using Local Instrumental Variables with an Application to Changes in College Enro… | 1.000 | 15 | 3 | 100% |
| 2 | Heckman, J. J. and E. Vytlacil (2005) Structural Equations, Treatment Effects, and Econometric Policy Evaluation | 1.000 | 7 | 3 | 100% |
| 3 | Carneiro, P., M. Lokshin, and N. Umapathi (2017) Average and Marginal Returns to Upper Secondary Schooling in Indonesia | 0.941 | 12 | 4 | 83% |
| 4 | Carneiro, P., J. J. Heckman, and E. Vytlacil (2010) Evaluating Marginal Policy Changes and the Average Effect of Treatment for Individuals at the Margin | 0.941 | 6 | 4 | 83% |
| 5 | Heckman, J. J. and E. Vytlacil (2001) Policy-Relevant Treatment Effects | 0.874 | 6 | 2 | 100% |
| 6 | Newey, W. K (1994) The asymptotic variance of semiparametric estimators | 0.811 | 4 | 2 | 100% |
| 7 | Robinson, P. M (1988) Root-N-consistent semiparametric regression | 0.811 | 4 | 2 | 100% |
| 8 | Heckman, J. J. and E. J. Vytlacil (1999) Local instrumental variables and latent variable models for identifying and bounding treatment effects | 0.693 | 5 | 1 | 100% |
| 9 | Björklund, A. and R. Moffitt (1987) The Estimation of Wage Gains and Welfare Gains in Self-Selection Models | 0.644 | 2 | 2 | 100% |
| 10 | Chernozhukov, V., D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, W… (2018) a): Double/debiased machine learning for treatment and structural parameters | 0.644 | 2 | 2 | 100% |
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